Download Annual Equity-Based Insurance Guarantees Conference

The Society of Actuaries and Annuity Systems Inc.
Present:
Annual Equity-Based
Insurance Guarantees Conference
November 17-18, 2014
Chicago, USA
Table of Contents
Corporate Sponsors..........................................................................................…..….....3
Presenter Biographies .......................................................................................….…....5
Attendee List .…....................................................................................................…….11
Agenda / Handouts….................................................................................................…18
The VA and FIA Landscape: A Reinsurer’s View…………………………...………………..20
Darryl Stewart & Markus Jaeger, Munich Re
The Last 10 Years of Retirement Products: A Rating Agency’s Viewpoint.........................34
Neil Strauss, Moody’s
History of Fixed Indexed Annuity Products ......................................……………..…………45
Alan Grissom, S&P Dow Jones Indices
Hedging Market Risks………………………………......................................……………………52
Matt McFarland, CBOE
Annuity Hedging: Impact on the Equity Derivatives market...............................................57
Phillipe Combescot, BNP Paribas
Variable Annuity (“VA”) Products Pricing Challenges and issues ……………………………68
Amit Ayer, Willis Re
Pricing Considerations for VA, FIA and VUL Products………………………………...............86
Tim Hill, Milliman
Risk Management Funds………….....................................................................................95
Marshall Greenbaum, AnchorPath Financial
Volatility Control – Optimising the overall product framework?........................................108
Stephen Einchcomb, Royal Bank of Scotland
Impact of Hedging on Capital and Reserves...................................................................121
Alex Marion, Numerix
FAS157, AG36, and Aligning Accounting Methodology with Hedging Strategy...............131
Brian Boucher, Transamerica
Influence of Computing and Models on Risk Management……………………………………142
Peter M. Phillips, AON Benfield
State-of-the-art Hybrid Modeling for Fixed Indexed Annuities and Variable Annuities…..165
Russell Goyder, FINCAD
Understanding and Managing Policyholder Behavioral Risks.........................................194
Ben Neff, GGY Axis; Tim Paris, Ruark Insurance Advisors
Panel Discussion with Chief Risk Officers (CROs)………………......................................222
Steve Ginnan, Nationwide Financial; John Rhodes. Lincoln Financial Group; Stephen Murphy,
Ohio National Financial Services
US Rates Outlook………………………..............................................................................224
Anshul Pradhan, Barclays
Changing Distribution Paradigm for Fixed Indexed Annuities and Variable Annuities.....241
Ramsey Smith, GoldmanSachs
The Last 10 Years of Retirement Products - A Consultant's Viewpoint………………….....250
Ken Mungan, Milliman
The Last 10 Years of Retirement Products – A Direct Writer/Hedger’s Viewpoint: VA
Hedging Lessons Learned ….…………………………………………………………………278
Andy Rallis, Metlife
Platinum Sponsors
Silver Sponsors
Presenter Biographies
Conference Chairman
Dr. K. (Ravi) Ravindran
President
Annuity Systems Inc. (ASI)
Dr. K. (Ravi) Ravindran currently spends much of his time lecturing, selectively consulting on VArelated issues and running a private equity fund. In addition to this, he holds a visiting professor
appointment in Haskolinn Reykjavik (Iceland) and is the author of the recently published book
titled The Mathematics of Financial Models.
As a pioneer to apply derivatives-based hedging techniques to manage market risks embedded in
VAs, Ravindran’s experience includes:
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Developing equity-based guarantee products and managing in excess of USD 100 billion
in account value for insurance companies selling these products;
Running exotic derivatives desk globally at Toronto Dominion Bank;
Manning the CEO position at RGA Financial Products (a subsidiary of Reinsurance
Group of America);
Holding past adjunct professorships at both the University of Waterloo and University of
Calgary;
Co-editing the well-received book titled VA: A Global Perspective;
Authoring the well-received book titled Customized Derivatives: A Step-by-Step Guide to
Using Exotic Options, Swaps and Other Customized Derivatives;
Serving as an associate editor of the well-received book titled Handbook of Derivatives;
and
Editing/authoring papers of which some have formed, and continue to form, part of the
professional exams in various societies.
Co-Moderator
Steve Fredlund FSA, MAAA, MBA
Allianz Investment Management
AIM Principal – Hedging
Steve Fredlund is an actuary within the Allianz Investment Management group of AllianzLife,
leading variable annuity hedge production for two European entities. He also volunteers as the
Executive Director of Our Response, a non-profit he co-founded focused on community
transformation work in Rwanda. Steve lives with his wife and three children in Minnesota.
Presenters
Amit Ayer, FSA, MAAA
Vice President, Senior Actuary – Life Solutions
Willis Re
Amit Ayer is based in the New York City office of Willis Re, as a Vice President, Senior Actuary
for the Life Solutions Group. He joined Willis Re in August 2014. Prior to joining Willis Re, Amit
worked at Ernst & Young, where he was the co-lead for the firm’s insurance risk management
practice. He led the firm’s variable annuity risk management group and was a key member of the
firm’s Enterprise Risk Management group. Prior to Ernst & Young, Amit also worked at an elite
management consulting firm, focusing on strategic implications around developing an insurer
capital model for companies under the SIFI designation. Amit also has also led the variable
annuity hedging function for a major multinational insurer, with responsibilities including trade
execution, hedging strategy, liability and asset modeling, and performance attribution reporting.
Amit is a frequent speaker at actuarial and risk management industry events. Amit is a Fellow of
the Society of Actuaries (FSA) and a Member of the American Academy of Actuaries (MAAA). He
holds a Masters in Biostatistics (Distinction) from Columbia University and a Bachelors in Human
Biology (Honors) from Brown University.
Russell Goyder, Ph.D.
Director of Quantitative Research and Development
FINCAD
Before joining FINCAD in 2006, Dr. Russell Goyder worked as a consultant at The MathWorks,
solving a wide range of problems in various industries, particularly in the financial industry. He
now manages FINCAD’s quant team and oversees the delivery of analytics functionality in
FINCAD’s products, from initial research to the deployment of production code. Dr. Goyder holds
a Ph.D. in Physics from the University of Cambridge.
Alan Grissom
Vice President – Insurance Channel Management
S&P Dow Jones Indices
Alan works in the S&P Dow Jones Indices Channel Management business as the Global Head of
Insurance. Alan strategically leads and directs the insurance lines of business for S&P Dow
Jones Indices. He has been with S&P Dow Jones Indices since 2010.
Prior to joining S&P Dow Jones Indices, Alan spent 12 years working in the insurance space with
some of the largest insurance carriers in the world. A successful serial entrepreneur, he was a
founding partner of a marketing consulting firm and the founder of a software development firm.
He also established and led the highly acclaimed Independent Distribution Channel at AIG
American General Life and Accident after guiding the annuity products division to record sales
and profits.
Alan has a master’s degree from Vanderbilt University’s Owen School of Management and a
bachelor’s degree from The University of Alabama.
Matt McFarland
Director of Business Development
Chicago Board Options Exchange (CBOE)
Matt McFarland works with insurance companies to enhance hedging strategies associated with
variable annuities and fixed indexed annuities. He also oversees CBOE’s FLEX options program,
the Exchange’s answer to the OTC market where users can customize key contract terms like
strike price, expiration date and exercise style.
In addition, Matt manages CBOE’s Customized Options Pricing Service (COPS), an end-of-day
valuation service for equity options.
Matt chairs CBOE’s Retail Advisory Committee and is a member of the Security Traders
Association’s Listed Options Committee. He has worked at CBOE since 2007. Prior to joining
CBOE, Matt worked in equity derivative sales at JP Morgan for ten years.
Matt graduated from Marquette University in 1992 and earned an MBA in Finance from DePaul
University in 2001.
Ken Mungan FSA, MAAA
Financial Risk Management Practice Leader
Milliman
Ken is the leader of Milliman Financial Risk Management, LLC, which he founded in 1998.
Milliman’s financial risk management business is the leading provider of hedging services to the
retirement savings industry. Milliman’s work helps the clients of life insurance companies, banks,
financial advisory platforms and mutual fund firms create strategies for success in retirement. In
particular, the Milliman Managed Risk Strategy provides retail investors with institutional quality
market risk management for $60 billion in variable annuity, 401(k) and retail mutual funds. In
addition, Milliman Financial Risk Management, LLC, serves as an investment advisor to provide
hedging services on $90 billion of assets on the balance sheets of financial institutions.
Ken has pioneered hedging techniques that are standard practice in the industry today. He and
his team have worked with most major life insurers to implement and operate hedging strategies
to protect the solvency of these institutions and to stabilize earnings.
His current focus is on expanding the availability of risk management techniques to give
individuals simple, transparent and reliable tools to manage market risks in retirement. Ken is a
frequent speaker at industry events and the author of many professional articles.
Stephen R. Murphy, FSA, MAAA
Senior Vice President, Capital Management
Ohio National Financial Services
Steve is Senior Vice President, Capital Management for Ohio National Financial Services. Steve
is an FSA and MAAA. Steve joined Ohio National from GE Financial Assurance in Lynchburg,
Virginia in 2002. Since then, Steve has held a number of positions at Ohio National including
responsibility for annuity product development. Steve’s responsibilities span annuity reinsurance,
enterprise capital including RBC, and thought leadership in the product area. Steve is also
involved in various industry work groups on variable annuities. Steve is a graduate of the The
Ohio State University.
Ben Neff, FSA
Vice President, US Business Development
GGY AXIS
Ben Neff joined GGY AXIS in 2009, and has been involved in a variety of marketing and support
activities focusing on ALM, equity linked products, and hedging. Ben started his career in the
Aerospace division of ITT Industries as a Simulation Engineer after obtaining Bachelor’s degrees
in Math and Physics from Indiana University in 2001. In 2006, he obtained an MBA from Purdue
University with a concentration in Finance and a fellowship in Marketing and Business
Development. Prior to joining GGY, Ben worked for Conseco Services in an ALM role.
Timothy Paris, FSA, MAAA
Chief Executive Officer
Ruark Insurance Advisors, Inc.
Tim leads RIA in providing consultative reinsurance brokerage services that help insurance
company clients better manage risks related to longevity, mortality, and guarantees to
policyholders. These efforts are complemented by the company’s industry-leading experience
studies of policyholder behavior.
Prior to joining the company in 2009, Tim was an officer at several insurance companies, with
responsibilities that included product management, pricing, valuation, expense management,
financial reporting and asset-liability management. Throughout his career, Tim has enjoyed
leadership roles in the development of innovative retirement savings and protection products.
Tim is a frequent speaker at industry events on the topics of longevity and mortality risk
management and policyholder behavior. His work and commentary have appeared in National
Underwriter, Investment News, American Banker, Annuity News, Retirement Income Journal,
Insurance Risk, The Actuary, Reinsurance News and Risk Management. Tim is a contributing
editor for The Actuary magazine and through the Ruark Insurance Advisors blog he is a
contributing member of the Society of Actuaries’ online outreach team for the SOA Riskpertise
website. He is also a member of Insurance Ireland's Business Advisory Panel.
Tim is a Fellow of the Society of Actuaries, a member of the American Academy of Actuaries and
a graduate of the University of Connecticut, where he earned a degree in Mathematics with high
honors.
Peter M. Phillips
Managing Director and Head of the Annuity Solutions Group
Aon Benfield
Peter since 1977 has developed an international track record of designing, implementing and
managing effective variable annuity hedge programs. Peter has over 25 years of derivative
trading, modeling and risk management experience in the banking and insurance industries.
The Annuity Solutions Group at Aon Benfield, provides clients with investment advisory and
consulting services, and PathWise software, an award winning integrated high performance
computing business solution to model, price, value, report and manage complex financial
guarantee risk embedded in Life Insurance products. It is offered to clients either as a Softwareas-a-Service solution in Amazon and/or a bare metal co-location solution , or as an on-premise
software where the client is responsible for the installation and administration of PathWise.
Peter obtained an honors MBA from the University of Chicago, and a Msc in Finance from the
London School of Economics, and an honors Bachelor of Commerce and Economics from the
University of Toronto. Peter holds Series 7, 24, 66, 79, 3 and 4 registrations with Aon Benfield
Securities, Inc.
Andrew D. Rallis, FSA, MAAA
Senior Vice President & Global Chief Actuary
MetLife, Inc.
Andrew D. Rallis is Senior Vice President and Global Chief Actuary and is responsible for the
practice of actuarial science across MetLife.
Andrew also serves as Chairman of MetLife’s captive reinsurers and serves on the company’s
employee benefits Investment Advisory Committee, as well as several internal asset/liability
management and risk management committees. He is a frequent speaker at industry meetings on
ALM and actuarial topics such as the risk management of variable annuities, the impact of low
interest rates on insurers and the transformation of actuarial functions.
Andrew joined the company in 1984 in MetLife's Actuarial Development Program. After attaining
Fellowship in the Society of Actuaries in 1992, he was honored with MetLife’s Bailie Award in
1993, awarded periodically to non-officer actuaries in recognition of high standard of
performance, sense of integrity and degree of dedication to the company's various businesses.
He has advanced through roles of increasing responsibility in pricing, valuation and ALM
departments and has extensive capital markets, US Generally Accepted Accounting Principles
(GAAP), statutory financial reporting and product development experience. He has participated in
the company's adoption of GAAP accounting, the demutualization and funding of the Closed
Block, the development of MetLife's economic capital model, and in the actuarial due diligence of
and purchase GAAP accounting for various acquisitions.
Andrew graduated from the Massachusetts Institute of Technology (MIT) in 1982 with a BS in
Physics and a BS in Mathematics. He is Fellow of the Society of Actuaries and a member of the
American Academy of Actuaries.
John M. Rhodes
Chief Risk Officer
Lincoln Financial Group
John M. Rhodes leads the risk organization at Lincoln Financial Group, where his team develops
methodologies and processes to measure, report and monitor risks throughout the organization.
John also leads the Equity Risk Management function, which includes variable annuity hedging
and guiding the corporation’s overall approach to managing equity product risk.
John joined Lincoln Financial Group in February 2009 as the head of Equity Risk Management.
Prior to joining Lincoln, he oversaw Hedging Operations and Performance Management for ING
US Financial Services. John has also held risk roles with JPMorgan Chase and GE Capital. Prior
to his corporate career, he served as an officer with the United States Navy, working as a
specialist in nuclear propulsion.
John holds a degree from the United States Naval Academy and an MBA from New York
University.
Neil Strauss, ASA, CERA
Vice President – Senior Credit Officer
Moody’s Investors Service
Neil Strauss is Vice President – Senior Credit Officer of the Financial Institutions Group of
Moody’s Investors Service. He is responsible for a portfolio of U.S. ratings within the Life
Insurance Group.
Prior to joining Moody’s in 2010, Neil worked for three decades in the insurance industry and as a
senior credit rating agency insurance and reinsurance sector analyst. He has held actuarial, risk
management and credit roles at New York Life, AXA-Equitable and AIG.
A graduate of Johns Hopkins University, Neil holds two professional designations from the
Society of Actuaries.
Attendee List as of
October 23, 2014
Sorted By
Last Name
Abiodun Afolabi
Heroes Insurance Brookers Limited
Ikeja, Nigeria
Rich Ash
Jackson National Life Insurance Co
Lansing, MI
Amit Ayer
Willis Re Inc
New York, NY
Zafar Bhatti
Manulife Financial
Toronto, ON
Steve Binioris
Jackson National Life Insurance Co
Lansing, MI
Matt Blanchette
Forethought
Simsbury, CT
Joshua Boehme
Jackson National Life Insurance Co
Lansing, MI
Brian Boucher
Transamerica Life & Protection
St Petersburg, FL
Joe Buffo
X-Change Financial Access (XFA)
CHICAGO, IL
Renee Cassel
Thrivent Financial
Minneapolis, MN
Simon Chan
Toronto, ON
Marie-Laure Chandumont
Barclays Capital
New York, NY
Hsiaohsu Chang
Cathay Life
Taipei, Taiwan
Yen Ming Chen
Cathay Life
Taipei, Taiwan
Naveed Choudri
Citigroup
New York, NY
Tom Christensen
Nationwide Financial
Columbus, OH
Ryan Christianson
S&P Dow Jones Indices
New York, NY
Philippe Combescot
BNP Paribas
New York, NY
Erin Conrad
Principal Financial Group
Des Moines, IA
Sufang Cui
MetLife
Morristown, NJ
Jim Cypert
Wells Fargo Reinsurance
Charlotte, NC
Tre DePietro
BNP Paribas
New York, NY
Harold Dershowitz
Data Life, Inc
Verona, NJ
Raynald Doyon
Industrial Alliance Insurance & Financial
Servic
Quebec, QC
Rajeev Dutt
Milliman Inc
Chicago, IL
Donald Dye
RBC Capital Markets
New York, NY
Jack Earl
Sun Life Financial
Waterloo, ON
Stephen Einchcomb
RBS
London, United Kingdom
Brandon Emerson
Penn Mutual Life Insurance Co
Horsham, PA
Seong-Min Eom
KPMG LLP
New York, NY
Kirk Evans
Sammons Retirement Solutions
West Des Moines, IA
Mark Evans
Applied Stochastic LLC
Louisville, KY
Peter Feng
Athene USA
West Des Moines, IA
Ilya Finkler
Global Atlantic Finanical Group
New York, NY
John Folkrod
Pacific Life Insurance Co
Newport Beach, CA
Lindsay Foster
Principal Financial Group
Des Moines, IA
Steve Fredlund
Allianz Life
Minneapolis, MN
Craig Fyfe
Sun Life Financial
Waterloo, ON
Rob Garfield
FINCAD
New York, NY
Bill Gaumond
Allianz Life
Minneapolis, MN
Steve Ginnan
Nationwide Financial
Columbus, OH
Russell Goyder
FINCAD
Surrey, BC
Marci Green
Goldman Sachs
New York, NY
Marshall Greenbaum
AnchorPath Financial
Stamford, CT
Alan Grissom
S&P Dow Jones Indices
New York, NY
June Guan
MetLife
Jersey City, NJ
Mark Hadley
Goldman Sachs
New York, NY
Alex Haugh
Barclays
New York, NY
Kelley Hebert
RBS Securities
Stamford, CT
Eric Henderson
Athene USA
West Des Moines, IA
Regynald Heurtelou
Metlife
Convent Station, NJ
Daniel Heyer
Nationwide Financial
Columbus, OH
Melissa Hidalgo
Lincoln Financial Group
Hartford, CT
Timothy Hill
Milliman Inc
Bannockburn, IL
Jason Hudes
Goldman Sachs Asset Management
New York, NY
Stefan Jaschke
Munich Re
Munich, Germany
Mike Kappos
Wells Fargo Reinsurance
Charlotte, NC
Mark Kinzer
Berkshire Hathaway Group
Brookfield, WI
Kathy Kohlstedt
Securian Financial Group
St Paul, MN
Jim Kosinski
The Hartford
Simsbury, CT
Ginny Kowalczyk
CBOE
Chicago, IL
Trevor Kreel
Manulife Financial
Toronto, ON
Sonin Kwon
MassMutual
Springfield, MA
Marshall Lagani
Denis Latulippe
Laval University - School of Actuarial
Science
Quebec City, QC
Christina Lee
Berkshire Hathaway Group,
Reinsurance Division
Toronto, ON
Crystal Li
BMO Life Assurance Co
Toronto, ON
Xianhui Lin
Prudential Financial
Hartford, CT
Shuo Liu
AIG Life and Retirement
Houston, TX
Michael Loftus
Ruark Insurance Advisors Inc
Simsbury, CT
Vivek Madlani
Royal Bank of Scotland
London, United Kingdom
Alex Marion
Numerix
New York, NY
Cedar Rapids, IA
Arielle Marom
Bnp Paribas Securities Corp.
New York, NY
Simon Martel
Standard Life Assurance Co
Montreal, QC
Angie Matthews
Jackson National Life Insurance Co
Lansing, MI
Wendy McCullough
Thrivent Financial for Lutherans
Minneapolis, MN
Matthew McFarland
Chicago Board Options Exchange
Chicago, IL
Zach Meier
Great-West Financial
Greenwood Village, CO
Ryan Mellott
Jackson National Life Insurance Co
Lansing, MI
Rachel Min
Prudential Insurance Company
Newark, NJ
Dennis Montagna
Richard Morris
RBC Capital Markets
New York, NY
Abbas Mujtaba
Lincoln Financial Group
Philadelphia, PA
Ken Mungan
Milliman Inc
Chicago, IL
Stephen Murphy
Ohio National Financial Services
Cincinnati, OH
Jordan Muse
Ohio National Financial Services
Cincinnati, OH
Benjamin Neff
GGY AXIS
Indianapolis, IN
William Ober
AIG
New York, NY
Senol Ozturk
Prudential Financial
Newark, NJ
Tim Paris
Ruark Insurance Advisors Inc
Simsbury, CT
Patrick Persons
Ameriprise Financial
Minneapolis, MN
Peter Phillips
Aon Benfield Canada ULC
Toronto, ON
Jason Pilling
Manulife Financial
Toronto, ON
Bryan Pinsky
AIG Life and Retirement
Woodland Hills, CA
Tracey Polsgrove
John Hancock USA
Boston, MA
Anshul Pradhan
Barclays Capital
New York, NY
New York, NY
Amna Qaiser
Goldman Sachs Asset Management
New York, NY
Andrew Rallis
Metropolitan Life Insurance Co
Bridgewater, NJ
Nikolaos Rapanos
Goldman Sachs
New York, NY
Gustavo Rapaport
FINCAD
Vancouver, BC
Marc Raphel
FINCAD
New York, NY
Ravi Ravindran
Annuity Systems Inc
Toronto, ON
Thomas Reedy
John Hancock Financial Services
Boston, MA
John Rhodes
Lincoln Financial Group
Philadelphia, PA
Alpesh Sanghani
Ernst & Young LLP
New York, NY
Stacey Schabel
Jackson National Life Insurance Co
Lansing, MI
Chad Schmitt
Thrivent Financial For Lutherans
Minneapolis, MN
Brian Schroeder
Jackson National Life Insurance
Company
Lansing, MI
Denis Schwartz
Lincoln National Life Insurance Co
Fort Wayne, IN
Michael Scriver
Allianz Life Insurance Co of North
America
Minneapolis, MN
Jee Shen
Wells Fargo Life & Annuity Reinsurance
Charlotte, NC
James Simons
Ameriprise Financial
Minneapolis, MN
Joel Smith
Jackson National Life Insurance
Company
Lansing, MI
Ramsey Smith
Goldman Sachs & Co
New York, NY
Ray Song Song
Protective Life
Birmingham, AL
Shirley Song Song
Milliman Inc
Chicago, IL
Christian Stauder
RBS
Stamford, CT
Darryl Stewart
Munich Re
Munchen, Germany
Neil Strauss
Moody's Investors Service
New York, NY
Peter Travnicek
Numerix LLC
Chicago, IL
Jeff Walentowski
Thrivent Financial for Lutherans
Appleton, WI
Brian Walta
Jackson National Life Insurance Co
Lansing, MI
Michael Warsh
Chicago Board Options Exchange
Chicago, IL
Joseph Weiss
Voya Financial
Windsor, CT
Paul Weissman
MetLife
Bloomfield, CT
Nathan Wilbanks
Nationwide Financial
Columbus, OH
John Wong
MetLife
New York, NY
Steve Wright
Ruark Consulting LLC
Simsbury, CT
Justin Wyant
Penn Mutual Life Insurance Co
Horsham, PA
Zhen Xiang
Transamerica Capital Management
Cedar Rapids, IA
Yiping Yang
Milliman Inc
Chicago, IL
Yi Yang
Guardian Life Insurance Co
New York, NY
Sanyueh yao
Metlife
Basking Ridge, NJ
Frankie Yung
RGA
Chesterfield, MO
Trevor Zeimet
Ameriprise Financial Inc
Minneapolis, MN
Frank Zhang
Pacific Life Insurance Co
Los Angeles, CA
Jim Zhou
Global Atlantic Financial Company
New York, NY
Weiqun Zhou
Lincoln Financial Group
Philadelphia, PA
Vadim Zinkovsky
MetLife
New York, NY
Monday, November 17, 2014
7:30 – 8:30 AM
Registration and Breakfast
8:30 – 8:40 AM
Opening Comments by Chairman
8:40 – 9:30 AM
The VA and FIA Landscape
Darryl Stewart, Munich Re
9:30 – 10:15 AM
The Last 10 Years of Retirement Products – A Rating Agency’s
Viewpoint
Neil Strauss, Moody’s
10:15 – 10:45 AM
Coffee Break
10:45 – 12:15 PM
1A – Risk Management Track: Hedging Market Risks
Phillipe Combescot, BNP Paribas; Matt McFarland, CBOE; Alan
Grissom, S&P Dow Jones Indices
1B - Pricing/Valuation Track: Pricing and Valuation Considerations
for VA, FIA and VUL Products
Amit Ayer, Willis Re; Tim Hill, Milliman
12:15 – 1:30 PM
Lunch
1:30 – 3:00 PM
2A - Risk Management Track: Volatility Control Funds
Stephen Einchcomb, Royal Bank of Scotland; Marshall Greenbaum,
AnchorPath Financial
2B - Pricing/Valuation Track: Impact of Hedging on Capital and
Reserves
Brian Boucher, Transamerica; Alex Marion, Numerix
3:00 – 3:30 PM
Coffee Break
3:30 – 5:00 PM
3A - Risk Management Track: Influence of Computing and Models
on Risk Management
Peter M. Phillips, AON Benfield; Russell Goyder, FINCAD
3B - Pricing/Valuation Track: Understanding and Managing
Policyholder Behavioral Risks
Ben Neff, GGY Axis; Tim Paris, Ruark Insurance Advisors
5:30 – 7:00 PM
Reception and Networking
Tuesday, November 18, 2014
7:30 – 8:30 AM
Breakfast
8:30 – 10:00 AM
Panel Discussion with Chief Risk Officers (CROs)
Steve Ginnan, Nationwide Financial; John Rhodes. Lincoln Financial
Group; Stephen Murphy, Ohio National Financial Services
10:00 – 10:30 AM
Coffee Break
10:30 – 11:15 AM
Rates Outlook
Anshul Pradhan, Barclays
11:15 – Noon
Changing Distribution Paradigm for Fixed Indexed Annuities and
Variable Annuities
Ramsey Smith, GoldmanSachs
Noon – 1:30 PM
Lunch
1:30 – 2:30 PM
The Last 10 Years of Retirement Products - A Consultant's
Viewpoint
Ken Mungan, Milliman
2:30 – 3:30 PM
The Last 10 Years of Retirement Products – A Direct
Writer/Hedger’s Viewpoint
Andy Rallis, Metlife
3:30 PM
Closing Comments by Chairman
K. Ravindran, Annuity Systems Inc.
12/8/2014
The VA & FIA Landscape: A Reinsurer’s View
10th Annual Equity Based Insurance Guarantees Conference (Chicago)
17 November 2014 (0840 – 0930 hours)
Darryl Stewart & Markus Jaeger
Disclaimer
This material is for distribution only under such circumstances as may be permitted by applicable
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any recipient.
It is published solely for informational purposes. No representation or warranty, either express or
implied, is provided in relation to the accuracy, completeness or reliability of the information
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markets or developments referred to in the materials. It should not be regarded by recipients as a
substitute for the exercise of their own judgement. Any opinions expressed in this material are
subject to change without notice and may differ or be contrary to opinions expressed by other
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Munich Re is under no obligation to update or keep current the information contained herein.
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Prior to entering into a transaction you should consult with your own legal, regulatory, tax,
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Distribution of this document does not oblige us to enter into any transaction. Any offer would be
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1
12/8/2014
Introduction to Munich Re
■ Who are we (in the context of equity based insurance guarantees)?
■ What have we been doing (recently)?
■ What would we like to share?
Feedback on
Market Views
and Trends
Economic
Observations
Reporting
Solutions
The VA & FIA Landscape: A Reinsurer’s View – Darryl Stewart & Markus Jaeger
17/11/2014
3
Feedback on Market Views & Trends
2
12/8/2014
Messages from the Market
1. End of the Arms Race
2. Volatility Control & De-Risking
3. Volume Management
4. Hedging Adequacy
5. Balance Sheet Proliferation
The VA & FIA Landscape: A Reinsurer’s View – Darryl Stewart & Markus Jaeger
17/11/2014
5
17/11/2014
6
1. End of the Arms Race
■ Unsupportable enhancement of
product terms has slowed
■ Frequency of update of terms has
reduced significantly
■ Product terms designed within risk
management constraints
■ But…still enough legacy missile
stockpiles to destroy the world?
The VA & FIA Landscape: A Reinsurer’s View – Darryl Stewart & Markus Jaeger
3
12/8/2014
2. Volatility Control & De-Risking
■ Explict: “Vol-Control” funds
■ Implicit: Limits on fund allocations
■ Reflects heightened post-crisis
appreciation of where Vol can go
■ Vol-Control funds creates some
challenges for conventional hedging
thinking (the hedge is in the fund!?!)
■ Also creates challenges with respect
to reserve and capital modeling
The VA & FIA Landscape: A Reinsurer’s View – Darryl Stewart & Markus Jaeger
17/11/2014
7
17/11/2014
8
3. Volume Management
■ Many players actually seeking to
limit business volumes to strategic
targets
■ Message 3 is arguably driver of
messages 1 and 2
■ Could discipline change if one or two
players (perhaps new players)
started to chase volume?
■ Accepted disconnect between
industry perception of volumes and
external perception of volumes
The VA & FIA Landscape: A Reinsurer’s View – Darryl Stewart & Markus Jaeger
4
12/8/2014
4. Hedging Adequacy
■ Consistently heard that people are
“satisfied” with their hedging
programs
■ Many different approaches (close
matching of Greeks, macro hedging
of tails)
■ Naturally, there is self-selection
■ No complacency, but palpable
confidence
■ However…
The VA & FIA Landscape: A Reinsurer’s View – Darryl Stewart & Markus Jaeger
17/11/2014
9
17/11/2014
10
5. Balance Sheet Proliferation
■ Too many balance sheets!
■ Challenge of instability across
different measures
■ Limitations to reflecting actual
hedging strategies in reporting
The VA & FIA Landscape: A Reinsurer’s View – Darryl Stewart & Markus Jaeger
5
12/8/2014
Messages from the Beyond the Market (FIA)
1. Barbarians at the Gate
■ Financial Sponsors / “Hedge Fundy” acquirors have leading bid for fixed
annuity business (whether indexed or not)
■ Insurance liabilities perceived by many non-insurance players as retail
term funding with a risk overlay
■ Pricing reflects view of investment returns available in excess of discount
rate (i.e. borrowing cost)
■ Interesting for Europeans – having spent the last 10 years trying to
obliterate spreads in excess of swaps as a component of Solvency 2
The VA & FIA Landscape: A Reinsurer’s View – Darryl Stewart & Markus Jaeger
17/11/2014
11
Economic Observations
6
12/8/2014
Operations / Hedging Platform
Hedging Platform – Main Components
Calypso
Data Warehouse
■ Front office system
■ Store and transform policy data
■ Store market data
■ Quality assurance of policy data
■ Pricing and Valuations
■ Biometric studies
■ Compute hedge sensitivities
■ Maintain IT-interfaces with clients
■ Perform daily hedging
■ Generate reports, e.g. limit system, risk
reports etc.
Java Library
■ Business and cash flow logic
■ Financial Models and their calibrations
■ Biometric models
The VA & FIA Landscape: A Reinsurer’s View – Darryl Stewart & Markus Jaeger
17/11/2014
13
Economic Risk Management
Challenges for hedging
■ Complexity of various balance sheets and
requirements
Hedging
Reporting
Product Design
■ Difficulties in getting sufficient credit for
hedging. Requires to implement and follow a
Clearly Defined Hedging Strategy (CDHS)
■ In recent market environments hedging tends
to increase statutory reserves but decrease
Total Asset Requirement (TAR)
■ Complexity of implementation of the CDHS in
corresponding cash flow models (nested
simulation)
The VA & FIA Landscape: A Reinsurer’s View – Darryl Stewart & Markus Jaeger
17/11/2014
14
7
12/8/2014
Statutory Reserve vs. Market Consistent Price
Case Study for a pure GMAB: CTE levels (based on the standard AAA scenario
generator) vs. Put Option Price
GMAB vs. Put Option (ATM)
GMAB vs. Put Option (80% ITM)
40
40
MC-Price
35
CTE 70
30
CTE 90
25
CTE 70
30
CTE 90
25
20
20
15
15
10
10
5
MC-Price
35
5
0
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Maturity (in years)
■ Statutory reserve and TAR favorable for longer
maturities due to high risk premium
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Maturity (in years)
■ Market consistent price more favorable for
deeper in-the-moneyness
■ Market consistent price competitive for short
maturities only
The VA & FIA Landscape: A Reinsurer’s View – Darryl Stewart & Markus Jaeger
17/11/2014
15
Statutory Reserve vs. Market Consistent Price
Case Study for a pure GMAB : Sensitivities of CTE 70 and Put Option
Delta (ATM)
Gamma (ATM)
0
-0.1
0.07
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
-0.2
CTE 70 - Gamma
0.05
-0.3
0.04
-0.4
-0.5
0.03
-0.6
0.02
-0.7
MC-Delta
-0.8
-0.9
-1
MC-Gamma
0.06
CTE 70 - Delta
0.01
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Maturity (in years)
Maturity (in years)
■ Low sensitivity of statutory reserve for longer
maturities due to high risk premium
■ Overall high Gamma profile for statutory
reserve
■ Comparably high Delta for short maturities
because of characteristics of the CTE
The VA & FIA Landscape: A Reinsurer’s View – Darryl Stewart & Markus Jaeger
17/11/2014
16
8
12/8/2014
Statutory Reserve vs. Market Consistent Price
Case Study for a pure GMIB: CTE levels and Sensitivities (65 year old male at
maturity)
GMIB Option
45
40
35
30
25
20
15
10
5
-
CTE 70 - Rho (100bps shift)
12
CTE 98
10
CTE 70
8
6
4
2
-
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Maturity (in years)
■ Higher steepness of CTE 70 compared to
GMAB option because of additional embedded
interest rate guarantee and mean reversion of
interest rates in the AAA Economic Scenario
Generator
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Maturity (in years)
■ Low Rho for long maturities due to high mean
reversion of interest rates
■ Significant Rho for short maturities since AAA
ESG is calibrated to current treasury yields
The VA & FIA Landscape: A Reinsurer’s View – Darryl Stewart & Markus Jaeger
17/11/2014
17
Statutory Reserve vs. Market Consistent Price
Conclusion for these simple Examples
■ High costs for hedges especially for longer term guarantees compared to
statutory reserve or total asset requirement.
■ High market sensitivities of statutory reserve for shorter maturities
■ Hedge guided by market consistent valuation does not allow to pick up risk
premium or mean reversion of interest rates
■ Tail hedging might look more effective in certain reporting regimes or for
additional needs, such as rating capital requirements
The economics of a real product might look very different because of
 large number of cash flows to be modeled, e.g. M&E fee, surrender charges,
reinsurance cash flows etc.
 assumptions on policyholder behavior and utilization of their options
 …
17/11/2014
9
12/8/2014
Reporting Solutions
Gaggle of Balance Sheets?
■ Gaggle
■ Herons
■ Siege
■ Swans
■ Murder
■ Ravens
■ Cackle
■ Parrots
■ Lamentation
■ Hyenas
■ Pandemonium
■ Geese
■ Unkindness
■ Crows
The VA & FIA Landscape: A Reinsurer’s View – Darryl Stewart & Markus Jaeger
17/11/2014
20
10
12/8/2014
If Portfolio is Hedged, Why is There a Problem?
■ Distinguish between truly hedged positions
and emergence of basis risk
■ If perfectly covered with a static hedge –
then there is no problem
■ If dynamically covered with a CDHS, then
impact is reduced by hedge effectiveness
coefficients
□ Philosophical discussion around reason
■ If dynamically covered without CDHS, then
uncovered from a reporting perspective
(relying on delta consistency through time)
The VA & FIA Landscape: A Reinsurer’s View – Darryl Stewart & Markus Jaeger
17/11/2014
21
Representation of Balance Sheets
Balance Sheet (AG43)
Balance Sheet (C3P2)
Segregated
Fund Assets
Policyholder
Account
Value
Segregated
Fund Assets
General
Account
Assets
Cost of
Guarantees
(CTE70)
General
Account
Assets
Policyholder
Account
Value
Cost of
Guarantees
(CTE90)
Balance Sheet (Rating Agency)
Segregated
Fund Assets
General
Account
Assets
The VA & FIA Landscape: A Reinsurer’s View – Darryl Stewart & Markus Jaeger
Policyholder
Account
Value
Cost of
Guarantees
(CTE98)
17/11/2014
22
11
12/8/2014
Can We Be of Assistance?
■ Can we codify the hedging program so we
know in all states of the world what hedges
the insurer is going to buy?
■ Can we price what the insurer is going to
buy within the existing reserving calculations
(considering the market parameters
projected within an ESG)
■ Can the reinsurer offer fixed pricing for
parameters not projected by ESG
■ Can we incorporate delivery of such a
package in a reinsurance contract
The VA & FIA Landscape: A Reinsurer’s View – Darryl Stewart & Markus Jaeger
17/11/2014
23
Impact on Balance Sheet
Opening Balance Sheet
Segregated
Fund Assets
Policyholder
Account
Value
General
Account
Assets
Cost of
Guarantees
Effect of Reinsurance
Closing Balance Sheet
Segregated
Fund Assets
Policyholder
Account
Value
Segregated
Fund Assets
Policyholder
Account
Value
Paid out as
Reinsurance
Premium
General
Account
Assets
Cost of
Guarantees
Reinsurer
Share of
Guarantee
Cost of
Guarantees
Obligation
from
Reinsurer
Reinsurer
Share of
Guarantee
The VA & FIA Landscape: A Reinsurer’s View – Darryl Stewart & Markus Jaeger
17/11/2014
24
12
12/8/2014
Representation of Balance Sheets (post Reinsurance)
Balance Sheet (AG43)
Segregated
Fund Assets
Policyholder
Account
Value
Reinsurer
Share of
Guarantee
Cost of
Guarantees
(CTE70)
Balance Sheet (C3P2)
Segregated
Fund Assets
Policyholder
Account
Value
Reinsurer
Share of
Guarantee
Cost of
Guarantees
(CTE90)
Balance Sheet (Rating Agency)
Segregated
Fund Assets
Policyholder
Account
Value
Reinsurer
Share of
Guarantee
Cost of
Guarantees
(CTE98)
The VA & FIA Landscape: A Reinsurer’s View – Darryl Stewart & Markus Jaeger
17/11/2014
25
What Have We Seen & What Do We Think?
Market Feedback
Economic Observations
1. End of the Arms Race
■ Challenges of CTE liability
valuation, versus market
valuation of hedging
instruments
2. Volatility Control & De-Risking
3. Volume Management
4. Hedging Adequacy
5. Balance Sheet Proliferation
6. Fixed Annuities as Alternative
Funding for Credit Focused
Financial Institutions
■ Challenges of hedging in the
face of AAA ESG drift and
mean reversion
Reporting Solutions
■ Reinsurance as reporting
stabilization tool
The VA & FIA Landscape: A Reinsurer’s View – Darryl Stewart & Markus Jaeger
17/11/2014
26
13
12/8/2014
Thank you for your attention!
14
Equity Based Insurance Guarantees:
A Rating Agency View
10th Annual Equity Based Insurance Guarantee Conference (Chicago)
0930 – 1015 Hours
NEIL STRAUSS, Vice President – Senior Credit Officer
17 November 2014
Agenda
1. Win-Win?
2. Post-Financial Crisis
3. De-risked Product Offerings
4. Legacy Block
5. Captives
6. Policyholder Behavior
7. What’s Changed? Stayed the Same?
Equity Based Insurance Guarantees: A Rating Agency View, November 2014
2
1
1
Win-Win?
Equity Based Insurance Guarantees: A Rating Agency View, November 2014
3
Ten years ago, selling a lot of retirement annuities to baby boomers
seemed to be a ‘win-win’ strategy for all. However, the financial crisis
and subsequent period of very low interest rates have necessitated a
modified view. The session will discuss the developments related to
variable annuities including such key topics as de-risked product
offerings, legacy block performance, captives and policyholder
behaviour. What has changed over the past 10 years and what has
stayed the same and how have the rating agency’s view of such
businesses evolved?
Equity Based Insurance Guarantees: A Rating Agency View, November 2014
4
2
Selling a lot of retirement annuities to baby boomers
seemed to be a ‘win-win’ strategy for all - that is until…
Interest Rates in the US Since 1960
Recent Equity Market Movements
Dow Jones Industrial Average
10 Year US Treasury
16
18,000
14
16,000
14,000
12
12,000
(%)
10
10,000
8
8,000
6
6,000
4
4,000
2
2,000
0
0
Source: Moody's Analytics
Source: Moody's Analytics
The financial crisis and subsequent period of very low interest rates necessitated a
modified view
Equity Based Insurance Guarantees: A Rating Agency View, November 2014
5
Selling a lot of retirement annuities to baby boomers
seemed to be a ‘win-win’ strategy for all
» Why a growth market for insurers?
– Positive demographic trends related to baby boomers’ retirement needs
– Social Security provides a safety net but more is needed
– Private pension plans less generous than in past
»
shift from “rich” defined benefit plans
» Insurers happy to fill the gap
» In reality, VA with living benefit guarantees – triple play
– For consumers: mutual fund + tax benefit + guarantee
– For insurers:
»
A. + Ice cream + sprinkles + toppings
»
B. - TARP entry ticket
»
C. - hole that can be filled by ‘wealthy Euro relatives‘
– For life actuaries:
»
Finally, a chance to show that P&C actuaries aren’t the only ones to experience ‘tail’ risks/’cat’ events
Equity Based Insurance Guarantees: A Rating Agency View, November 2014
6
3
2
Post-Financial Crisis
Equity Based Insurance Guarantees: A Rating Agency View, November 2014
7
Reassessments post-financial crisis
» Some pulled out totally
» Some pulled back
» Many de-risked new products
» Some offered non-guarantee sleeves
» More hedging
» Higher prices
» Products with internal balancing
» Less non proprietary sales
» Few new entrants
Equity Based Insurance Guarantees: A Rating Agency View, November 2014
8
4
VA Blocks in Much Better Shape; Market Correction Risk
S&P 500 Index
VA GLB Net Amount at Risk (in $ bn)
S&P 500
MetLife
2500
Prudential
Jackson
Lincoln
$8.0
$7.0
2000
$6.0
$5.0
1500
$4.0
1000
$3.0
$2.0
500
$1.0
0
$0.0
2009
Source: Moody's Analytics
2010
2011
2012
2013
Source: Company 10Ks
» Rising equity markets have lifted fees and reduced Net Amount at Risk (NAR)
exposures
– ROEs on VA blocks exceed expectations as long as market growth > reserve assumption
– But next market correction would restore NAR exposures and potential for losses
Equity Based Insurance Guarantees: A Rating Agency View, November 2014
9
VA Blocks in Much Better Shape; Market Correction Risk
(cont’d)
S&P 500 Volatility Index
S&P 500 VIX
Jun-14
Sep-14
Mar-14
Dec-13
Jun-13
Sep-13
Mar-13
Dec-12
Jun-12
Sep-12
Mar-12
Dec-11
Jun-11
Sep-11
Mar-11
Dec-10
Jun-10
Sep-10
Mar-10
Dec-09
Jun-09
Sep-09
Mar-09
Dec-08
Jun-08
Sep-08
Mar-08
Dec-07
Jun-07
Sep-07
Mar-07
Dec-06
Jun-06
Sep-06
Mar-06
Dec-05
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Source: Moody's Analytics
» Low market volatility reduces cost of hedging, supports earnings
» Hedging continues to advance
– Still under-hedging interest rate risk (rho); “Low for long” problematic
– Divergent approaches to hedge objective
» De-risking discipline through product design and re-pricing continues to improve risk
profile of overall VA block as lower risk products become a bigger portion of the overall
block
Equity Based Insurance Guarantees: A Rating Agency View, November 2014
10
5
3
De-Risked Product Offerings
Equity Based Insurance Guarantees: A Rating Agency View, November 2014
11
De-risked Product Offerings
» Throughout the middle of the last decade, VA products’ guarantees became
‘richer’
– Higher interest rate guarantees
» Financial crisis showed these benefits were underpriced
– History repeats itself – parallel with 2002 / 2003
» Post financial crises prices rose, interest rate guarantees were brought down
and asset allocation options limited
Equity Based Insurance Guarantees: A Rating Agency View, November 2014
12
6
4
Legacy Blocks
Equity Based Insurance Guarantees: A Rating Agency View, November 2014
13
Top 10 2013 US Variable Annuity Assets* by Issuer, $
billions
Rank
Issuer
Insurance Financial Strength Rating **
Assets
Market
Share
1
TIAA-CREF
Aa1 stable
$436
23%
2
MetLife
Aa3 stable
$180
10%
3
Prudential
A1 stable
$150
8%
4
Jackson National
A1 stable
$115
6%
5
Lincoln National
A1 stable
$112
6%
6
AXA Equitable
Aa3 stable
$105
6%
7
SunAmerica/VALIC
A2 stable
$94
5%
8
Ameriprise Financial
Aa3 stable
$74
4%
9
Voya
A3 positive
$71
4%
10
Hartford
A3 stable***
$62
3%
Top 10
$1,399
Total Industry
$1,868
Source: Morningstar. Data representative of Morningstar VA universe. New contracts may not be included in totals.
* VA assets shown includes assets with and without guaranteed living benefits, e.g.
TIAA
** Insurance financial strength rating, outlook for lead life insurance co. as of October
21, 2014
***Rating on Hartford's VA subsidiary Hartford Life and Annuity Insurance Co, is Baa2
stable
Performance Issues?
Equity Based Insurance Guarantees: A Rating Agency View, November 2014
14
7
5
Captives
Equity Based Insurance Guarantees: A Rating Agency View, November 2014
15
VA Recent Research on Onshore Captives
» Captive reinsurers have been criticized for regulations and accounting regimes by
incorporating in more loosely regulated domiciles, particularly offshore
» With mounting regulatory pressure, companies are proactively “re-domesticating” their
captives to onshore
» We believe that with relatively accommodative regulation in certain US states popular as
captive domiciles, on-shoring will not address the main credit risks of captives
» There is heavy industry reliance on onshore captives
» Only recapture to a traditionally regulated life insurer or voluntary strengthening of
reserves, capital, and investments will remedy shortcomings
» Regulators disagree about how to reduce reliance on captives – both the proposed
approaches will likely address new business, not the in-force business reliant on captives
Equity Based Insurance Guarantees: A Rating Agency View, November 2014
16
8
6
Policyholder Behavior
Equity Based Insurance Guarantees: A Rating Agency View, November 2014
17
Policyholder Behavior Remains a Wildcard
Group
Insurance Financial
Strength Rating
Recent Charge
Comment
AXA Financial
Aa3 Stable
> $1 bil in 2011
Reserve charge due to lower than
expected lapse rates & partial withdrawals
VOYA
A3 Positive
> $1 bil in 2011
Reserve charge due to lower lapse rates
MetLife Inc.
Aa3 Stable
4Q12: $752 mil
Lower than expected realized returns over
the life of guaranteed VA policies
Prudential Financial Inc.
A1 Stable
3Q13: $1.7 bil
Largely driven by updated lapse
assumptions based on actuarial review
» Policyholder behavior is unhedgeable
» Actual behavior experience: limited data thus far – time will tell
» Accounting charges for policyholder behavior assumption changes are very subjective
» Policyholders & distributors likely to become increasingly more efficient
Equity Based Insurance Guarantees: A Rating Agency View, November 2014
18
9
What’s changed? Stayed the same?
» Top Players musical chairs over time
» Less European interest
» Prices up
» Annuity business model success not guaranteed notwithstanding demographics
» State profitability of legacy block?
» M&A options limited
» More hedging but …
» Lesson - You can hedge some of the risk some of the time but not all of the risk all of the
time - Stat / GAAP / IFRS
» New products on the horizon?
» Actuaries and investment folks on same page now?
Equity Based Insurance Guarantees: A Rating Agency View, November 2014
19
Equity Based Insurance Guarantees: A Rating Agency View, November 2014
20
Neil Strauss
VP-Sr. Credit Officer
7 WTC @ 250 Greenwich Street
New York, NY 10007
10
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indirectly disseminate this document or its contents to “retail clients” within the meaning of section
761G of the Corporations Act 2001. MOODY’S credit rating is an opinion as to the creditworthiness of
a debt obligation of the issuer, not on the equity securities of the issuer or any form of security that is
available to retail clients. It would be dangerous for “retail clients” to make any investment decision
based on MOODY’S credit rating. If in doubt you should contact your financial or other professional
adviser.
Equity Based Insurance Guarantees: A Rating Agency View, November 2014
21
11
11/28/2014
History of Fixed Indexed Annuity Products
A PRESENTATION FOR SOCIETY OF ACTUARIES
EQUITY BASED INSURANCE GUARANTEES CONFERENCE 2014
J. Alan Grissom
10th Annual Equity Based Insurance Guarantee Conference (Chicago)
Session 1A
17 November, 2014 (1045 – 1215 hours)
For Financial Professionals. Not for Public Distribution.
PROPRIETARY. Permission to reprint or distribute any content from this presentation requires the written approval of S&P Dow Jones Indices.
U.S. FIXED INDEXED ANNUITY PRODUCT SALES
$50
$45
$40
$35
$30
$25
$20
$15
$10
$5
$0
38.6
32.1
27.3
23.4
11.7
5.2
5.4
32.4
34.0
29.5
25.3
25.0
26.7
23.4
14.0
6.5
FIA Sales
Source: Wink’s Sales and Market Report, Q2 2014
For Financial Professionals. Not for Public Distribution. PROPRIETARY. Permission to reprint or distribute any content from this presentation requires the written approval of S&P Dow Jones Indices.
2
1
11/28/2014
10 YEAR TREASURY
10 Year Treasury Yield
18
16
14
12
10
8
6
4
2
9/16/14
9/16/13
9/16/12
9/16/11
9/16/10
9/16/09
9/16/08
9/16/07
9/16/06
9/16/05
9/16/04
9/16/03
9/16/02
9/16/01
9/16/00
9/16/99
9/16/98
9/16/97
9/16/96
9/16/95
9/16/94
9/16/93
9/16/92
9/16/91
9/16/90
9/16/89
9/16/88
9/16/87
9/16/86
9/16/85
9/16/84
9/16/83
9/16/82
9/16/81
9/16/80
0
Source: Bloomberg, October 14, 2014
3
For Financial Professionals. Not for Public Distribution. PROPRIETARY. Permission to reprint or distribute any content from this presentation requires the written approval of S&P Dow Jones Indices.
EQUITY MARKETS
90
2500
80
2000
70
60
1500
50
40
1000
30
20
500
10
0
0
10 Year Treasury Yield
VIX
S&P 500
Source: Bloomberg, October 14, 2014. Past performance is not a guarantee of future results.
For Financial Professionals. Not for Public Distribution. PROPRIETARY. Permission to reprint or distribute any content from this presentation requires the written approval of S&P Dow Jones Indices.
4
2
11/28/2014
U.S. FIXED INDEXED ANNUITY PRODUCT SALES
$50
$45
$40
$35
$30
$25
$20
$15
$10
$5
$0
FIA Issued Premium
Buyer Statistics
38.6
27.3
23.4
11.7
2.8
25.3 25.0
26.7
32.1
29.5
32.4
• 63 Year Old Male
34.0
23.4
• $250k of Investable Assets
• $82,114 Average Fixed Indexed Annuity
Premium
14.0
6.5
4.3 5.1 5.4
• Safety Is Key Factor
Sales By Distribution
100.00%
80.00%
Sales By Surrender Charge
Schedule
87.30%
80.40%
60.00%
40.00%
20.00%
11.10%
7.80%
0.00%
Agency
Bank
2Q13
1.60%4.60% 3.30%3.90%
Broker Dealer
70.00%
60.00%
50.00%
40.00%
30.00%
20.00%
10.00%
0.00%
Career
2Q14
< 6 Yr 7 Yr 8-9 Yr 10 Yr 11 - 13 14 Yr 15- 16 Two
Yr
Yr
Tier
2Q13
2Q14
Source: Wink’s Sales and Market Report, Q2 2014
For Financial Professionals. Not for Public Distribution. PROPRIETARY. Permission to reprint or distribute any content from this presentation requires the written approval of S&P Dow Jones Indices.
5
AVERAGE FIA COMMISSIONS – AGENT LEVEL
10.00%
9.00%
8.00%
7.00%
6.00%
5.00%
2Q04
3Q04
4Q04
1Q05
2Q05
3Q05
4Q05
1Q06
2Q06
3Q06
4Q06
1Q07
2Q07
3Q07
4Q07
1Q08
2Q08
3Q08
4Q08
1Q09
2Q09
3Q09
4Q09
1Q10
2Q10
3Q10
4Q10
1Q11
2Q11
3Q11
4Q11
1Q12
2Q12
3Q12
4Q12
1Q13
2Q13
3Q13
4Q13
1Q14
2Q14
4.00%
Source: Wink’s Sales and Market Report, Q2 2014
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6
3
11/28/2014
TRADITIONAL FIXED DEFERRED ANNUITY
Bonus Rate
+
Declared Rate
=
Total Rate of
Return
For Financial Professionals. Not for Public Distribution. PROPRIETARY. Permission to reprint or distribute any content from this presentation requires the written approval of S&P Dow Jones Indices.
7
TRADITIONAL FIXED DEFERRED ANNUITY
Bonus Rate
+
=
Declared Rate
And/Or
Indexed Rated
Total Rate of
Return
For Financial Professionals. Not for Public Distribution. PROPRIETARY. Permission to reprint or distribute any content from this presentation requires the written approval of S&P Dow Jones Indices.
8
4
11/28/2014
PRODUCT EVOLUTION
38.6
•
•
•
29.5
27.3
11.7 • More Carriers
5.4
•
•
•
•
•
23.4
GLWBs Introduced
Shorter Surrender Charges
Lower Bonus Rates
More Uniform Crediting Methods
More Indices
1998
5.1
• More Products
25.0
32.4
• More Complex Indices
• Risk Control
• Smart Beta
2008
4.3
2.8
6.5
25.3
32.1
26.7
2014
Few Products Available1
Long Surrender Charges – Up to 17 Years2
High Bonus Rates – Up to 12.5%1
High Commissions – 36% were greater than
23.4
11% in 20011
Tiered Structures1
Few Indices1
Proprietary Crediting Methods1
14.0
•
•
•
•
34.0
Source: 1Index Compendium 2001Q2
2Index Compendium October 2014
For Financial Professionals. Not for Public Distribution. PROPRIETARY. Permission to reprint or distribute any content from this presentation requires the written approval of S&P Dow Jones Indices.
9
TRADITIONAL FIXED INDEXED ANNUITY
Bonus Rate
+
=
Declared Rate
And/Or
Indexed Rate
Total Rate of Return
S&P 500
S&P Midcap 400
DJIA
Russell 2000
Risk Control
Smart Beta
For Financial Professionals. Not for Public Distribution. PROPRIETARY. Permission to reprint or distribute any content from this presentation requires the written approval of S&P Dow Jones Indices.
10
5
11/28/2014
INDEXED RATE CREDITING
Crediting Methods
Participation Rates
•
Point To Point
Monthly
Quarterly
Annual
> Annual (Biannual, 5 Year, etc.)
•
-
Averaging
Daily
Monthly
•
•
•
•
Hedging Methods
Dynamically
-
Statically
Vanilla
Cliquets
Asians
Performance Trigger
For Financial Professionals. Not for Public Distribution. PROPRIETARY. Permission to reprint or distribute any content from this presentation requires the written approval of S&P Dow Jones Indices.
11
GENERAL DISCLAIMER
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Trademarks have been licensed to S&P Dow Jones Indices LLC. Redistribution, reproduction and/or photocopying in whole or in part are prohibited without written permission. This document
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12
6
11/28/2014
THANK YOU
Contact Us
Alan Grissom
[email protected]
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13
7
11/28/2014
Hedging Market Risks
10th Annual Equity Based Insurance Guarantee Conference (Chicago)
Session 1A
November 17, 2014 (1045 – 1215 hours)
Copyright © 2014 CBOE
Forward Looking Statements
Options involve risk and are not suitable for all investors. Prior to buying or selling an
option, a person must receive a copy of Characteristics and Risks of Standardized
Options. Copies are available from your broker, by calling 1-888-OPTIONS, or from The
Options Clearing Corporation at www.theocc.com. Futures trading is not suitable for all
investors, and involves risk of loss. The information in this document is provided solely
for general education and information purposes. No statement within this document
should be construed as a recommendation to buy or sell a security or future or to
provide investment advice. Past performance does not guarantee future results.
Supporting documentation for any claims, comparisons, statistics or other technical data
in this document is available from CBOE upon request. CBOE®, Chicago Board Options
Exchange®, CFE®, FLEX® and Execute Success® are registered trademarks and SPX
is a service mark of Chicago Board Options Exchange, Incorporated (CBOE). S&P®
and S&P 500® are trademarks of Standard & Poor's Financial Services, LLC and have
been licensed for use by CBOE and CBOE Futures Exchange, LLC (CFE). Financial
products based on S&P indices are not sponsored, endorsed, sold or promoted by
Standard & Poor’s, and Standard & Poor’s makes no representation regarding the
advisability of investing in such products.
Copyright © 2014 CBOE. All rights reserved.
CBOE HOLDINGS
2
1
11/28/2014
Agenda
Evolution of annuity hedging over the past 10 years from an
exchange perspective
What to expect going forward
3
CBOE HOLDINGS
Hedging Market Risks
History
Annuity hedging before the 2008 financial crisis
Limited hedging alternatives for insurers, especially for static hedging
Post 2008
Heightened counterparty concerns
Regulators push for increased transparency and centralized clearing
Insurance industry seeks hedging solutions from exchanges
CBOE HOLDINGS
4
2
11/28/2014
Hedging Market Risks
History (continued)
Exchanges not fully
equipped to solve insurer
hedging needs
Limited expiration dates
FLEX® option restrictions:
–
–
–
–
“Blackout dates”
Minimum size requirements
Maximum expiration dates
Strike precision
5
CBOE HOLDINGS
Hedging Market Risks
History (continued)
CBOE & insurance industry
successfully amend FLEX
option trading rules
Results
Changes implemented in 2010
Results obtained from CBOE
White Paper: “Hedge
Execution Comparison Test”
CBOE HOLDINGS
6
3
11/28/2014
Hedging Market Risks
VA Hedging
Variance Swaps
Variance Futures (old)
Quoted in volatility points
Size expressed in vega
notional
Quoted in variance units
Size expressed in contract
amounts
Margin requirements
uncompetitive
Variance Futures (new)
Quoted in volatility points
Size expressed in vega
notional
Margin requirements
competitive
Expiries match SPX listing
cycle
Source: JP Morgan paper: Just what you need to know about Variance Swaps”,
2005, Exhibit 1.1.2
7
CBOE HOLDINGS
Hedging Market Risks
Limitations of exchange-traded hedging solutions
Plain vanilla options only
Liquidity challenges on longdated options
Higher capital requirements
Symbol
P/C
Month
Day
Year
2SPX
2SPX
2SPX
2SPX
2SPX
2SPX
4SPX
4SPX
4SPX
4SPX
2SPX
2SPX
2SPX
4SPX
4SPX
2SPX
2SPX
2SPX
2SPX
4SPX
2SPX
2SPX
P
P
P
P
P
P
P
C
P
P
P
P
P
P
C
P
P
P
P
P
P
P
6
12
12
12
12
12
7
7
7
11
6
12
12
8
8
12
12
12
12
10
12
12
19
18
18
18
18
19
9
21
21
4
18
17
18
19
19
16
16
16
16
7
15
15
2020
2020
2020
2020
2020
2020
2020
2020
2020
2020
2021
2021
2021
2021
2021
2022
2022
2022
2022
2022
2023
2023
Strike
Price
1665.00
1000.00
1760.00
2480.00
2580.00
1000.00
1072.00
1073.60
1073.60
1215.96
1665.00
1740.00
1281.00
674.64
1911.48
1770.00
1860.00
1960.00
2580.00
1757.07
1980.00
2580.00
Mark
Open
Price
Interest
248.1266
351
79.4453 2,600
296.8604
45
668.6875
9
734.1469
68
79.4813 2,600
86.4839
233
848.8419
140
87.3183
140
123.1096
206
272.2795
179
311.6737
77
160.2306
800
33.0096
444
399.8329
444
345.1371 2,600
381.9926
43
425.2574
3
748.4911
12
335.9647
154
453.2743
3
756.1642
11
Source: Options Clearing Corp.
CBOE HOLDINGS
8
4
11/28/2014
Hedging Market Risks
Next phase of exchange-traded hedging solutions
Asian & Cliquet Index FLEX Options
Source: CBOE
9
CBOE HOLDINGS
Matt McFarland
[email protected]
5
28/11/2014
Annuity Hedging
Impact on the Equity Derivatives market
Philippe COMBESCOT,
Managing Director
Global Equity & Commodity Derivatives
th
10 Annual Equity Based Insurance Guarantee Conference (Chicago)
Session 1A: 17 November 2014 (1045 – 1215 hours)
FOR INSTITUTIONAL INVESTORS WHO ARE ELIGIBLE CONTRACT PARTICIPANTS ONLY - NOT FOR USE WITH RETAIL INVESTORS
Annuity Sales over time
FOR INSTITUTIONAL INVESTORS WHO ARE ELIGIBLE CONTRACT PARTICIPANTS ONLY - NOT FOR USE WITH RETAIL INVESTORS
Over the last 10 years annuity sales and the split between fixed and variable have been roughly stable. But
significant change within each category :
 VA separate account went from 55% to 81% of Variable Annuities sales
 Indexed Annuities went from 16% to 60% of total Fixed Annuity sales
Annuity Sales per quarter over the last 10 years
80
70
60
$ Billions
50
40
Variable
Fixed
30
20
10
0
2003 1Q 2004 1Q 2005 1Q 2006 1Q 2007 1Q 2008 1Q 2009 1Q 2010 1Q 2011 1Q 2012 1Q 2013 1Q 2014 1Q
Source: LIMRA.
2
1
28/11/2014
Insurance companies use of derivatives
FOR INSTITUTIONAL INVESTORS WHO ARE ELIGIBLE CONTRACT PARTICIPANTS ONLY - NOT FOR USE WITH RETAIL INVESTORS
Derivative hedging per risk type

Insurance companies use derivatives to hedge :
 Interest rate risk : 56% of the total derivatives notional. 75% is
FX
18%
through swaps, the rest through Caps and Floors
 Equity risk : 22% of derivatives notional. 80% of equity risk hedging
is done through options. The rest through futures and Total Return
Swaps.
Rates
56%
Equity
22%
 Foreign Exchange risk : 18% of derivatives notional
Source: Schedule DB, NAIC
Equity option type

Equity option position : 400 billion notional
 Calls : 31% of total notional. Average maturity is 1.5 years. Mainly
16%
Call
31%
18%
used to hedge Fixed Indexed Annuity crediting.
Put
 Puts : 35% of total notional. Average maturity is 5 years. Underlying
Collar
is mainly S&P 500. Used to hedge Variable Annuities GLBs
Other
35%
Source: Schedule DB
3
Variable Annuity guaranteed living benefits
FOR INSTITUTIONAL INVESTORS WHO ARE ELIGIBLE CONTRACT PARTICIPANTS ONLY - NOT FOR USE WITH RETAIL INVESTORS
Q2 2014 GLB election rate

Guaranteed living benefits still popular.
 Out of $27 billions variable annuity sales over
GLB
Available
88%
Q2 2014 guaranteed living benefits were
elected for $18.5 billions.
GLB
Elected
76%
 When GLBs are available election rate has
been declining since 2012 from 90% to
currently 75%.
 GLWB remains the most popular type of
Source: LIMRA
benefits representing 82% followed by GMIB
for 13%.
VA Assets with GLB elected
900
Others
GMIB
GLWB
800
700
Assets in bln
600

VA assets with GLBs growing steadily
 VA assets with GLWB assets elected
represents 62%.
500
 GLWB assets increased 2.5 times since the
end of 2009 vs 1.75 increase in the S&P 500.
400
 Very little increase for other types of GLB.
300
200
100
0
Q4 2008 Q4 2009 Q4 2010 Q4 2011 Q4 2012 Q4 2013 Q2 2014
Source: LIMRA.
4
2
28/11/2014
InsCo equity derivatives option position
FOR INSTITUTIONAL INVESTORS WHO ARE ELIGIBLE CONTRACT PARTICIPANTS ONLY - NOT FOR USE WITH RETAIL INVESTORS
Total current exposure through S&P 500 puts
in $ mln
< 1.5y
1.5y - 5y

> 5y
Notional
75,041
17,250
19,629
Delta
(6,058)
(2,165)
(3,417)
Vega
72
60
147
Avg strike
75%
65%
64%
Largest position relative to the overall equity
derivatives market is created by S&P 500 puts,
specifically on the long term.
 Largest vega position.
 Large forward position
 Total gamma exposure : $ 600 mlns
Source: Schedule DB
S&P 500 vega bought per quarter for maturities > 5y
25
175%
22.4
21.4
Vega Traded in $mm
155%
145%
15
12.8
13.5
135%
125%
10
115%
6.1
6.0
3.9
5
1.7
5.1
105%
95%
2.9
85%
0.8
0
75%
2011Q4*
2012Q2*
2012Q4*
Vega traded (mlns)
2013Q2
SPX perf (RHS)
2013Q4
Performance Since 30 Sep 2011
165%
20

The amount of long term S&P 500 vega
bought by insurance companies has
significantly decreased since the end of
2012 .

This is inline with the rise of the Equity
market and long term interest rates.
2014Q2
10Y Rates perf(RHS)
Source: Schedule DB
5
Impact on S&P500 long term volatility
FOR INSTITUTIONAL INVESTORS WHO ARE ELIGIBLE CONTRACT PARTICIPANTS ONLY - NOT FOR USE WITH RETAIL INVESTORS
Long term S&P 500 volatility has decreased significantly since 2012 as well.
S&P500 volatility still has a steep term structure that appeared mid 2005.
S&P 500 implied volatility over the last 10 years
60%
1y
50%
5y in 5y
40%
30%
20%
10%
0%
Nov-03
Nov-04
Nov-05
Nov-06
Nov-07
Nov-08
Nov-09
Nov-10
Nov-11
Nov-12
Nov-13
Nov-14
Source: BNP Paribas. Past performance is not indicative of future performance
6
3
28/11/2014
Comparison on implied volatility term structure
FOR INSTITUTIONAL INVESTORS WHO ARE ELIGIBLE CONTRACT PARTICIPANTS ONLY - NOT FOR USE WITH RETAIL INVESTORS
Current implied volatility term structure

Implied volatility
30%
Implied volatility term structure significantly
different across the main indices :
25%
 S&P 500 term structure is very steep
20%
 EuroStoxx50 term structure is close to flat and
compared to EuroStoxx50 and Nikkei.
S&P 500
15%
EuroStoxx50
below S&P 500 beyond 5 years. Most long
term structured products on EuroStoxx50 are
sellers of volatility.
Nikkei
 On average Nikkei implied volatility is higher
and close to flat. It exhibits a slight dimple
around the 5y maturity, the standard maturity
of Uridashis.
10%
0
2
4
6
8
10
Maturity in years
Source: BNP Paribas
5y in 5y minus 1y implied volatility spread

15%
10%
The difference between S&P 500 and
EuroStoxx50 term structure increased after the
2008 financial crises :
 Increased long term put buying from InsCo on
5%
S&P 500.
 Since 2008, given the level of interest rates,
0%
long term structured products on EuroStoxx50
are volatility sellers to generate attractive
coupon.
S&P 500
-5%
EuroStoxx50
-10%
Nov-03
Nov-05
Nov-07
Nov-09
Nov-11
Nov-13
Source: BNP Paribas.
7
End users of long term volatility across regions
FOR INSTITUTIONAL INVESTORS WHO ARE ELIGIBLE CONTRACT PARTICIPANTS ONLY - NOT FOR USE WITH RETAIL INVESTORS


US market : Variable annuities living benefits

GLxB policy holder is buying an exotic put from the insurance company.

Very long effective maturity

Insurance company hedging driven economic & regulatory
Europe and Japan : Structured notes


Retail clients

100% Capital Protection

Standard structure : Zero coupon + buy a call option
Private banking clients

Conditional capital protection : 65% to 85% barrier

Standard structure : Reverse convertible : Zero coupon + sell a put option

Auto callable is a very popular variation ( Uridashi in Japan)
Investing in Notes could result in trading losses. please refer to the risk disclaimer on page 14 for more details.
8
4
28/11/2014
Auto-callable structured notes
FOR INSTITUTIONAL INVESTORS WHO ARE ELIGIBLE CONTRACT PARTICIPANTS ONLY - NOT FOR USE WITH RETAIL INVESTORS

Auto-Callables are the most popular long term structure sold to end investors in Europe and Asia
over the last few years.
 On Observation Dates (typically yearly), if the Index is above its Initial Level, a high coupon is paid and the
structure ends early.
 If at maturity, the Index has not dropped below a predefined level and the structure has not been terminated
early the investor gets his principal back
 However, if the Index dropped below the predefined level at one point before maturity the investor ends up long
the index rather than cash.
Typical scenarios for auto-callable structures
Cancelled with coupon
Synthetically the note buyer is selling a strip of
puts spread across maturities weighted by the
probability of early redemption at that date.




To hedge the seller (IB) has to
Sell long term volatility
Buy long term forwards
100
Long the index
at maturity
Barrier
Observation
1
Investing in Notes could result in trading losses. please refer to the risk disclaimer on page 14
Observation
2
Observation
3
Observation
4 (Maturity)
Source : BNP Paribas. Graphical illustration is included for illustrative purposes only
9
European structured product market
FOR INSTITUTIONAL INVESTORS WHO ARE ELIGIBLE CONTRACT PARTICIPANTS ONLY - NOT FOR USE WITH RETAIL INVESTORS
Number of issuance per capital protection type

SPs issuance by Capital Protection
 From 2007 to 2009, higher level of
protection of initial capital invested due to the
increase in risk aversion
 Since then, with a sustainable low rates
environment, higher proportion of structures
with invested capital at risk (pre Lehman
proportion)
Source: BNP Paribas
Source: BNP Paribas
Number of issuance per maturity

SPs issuance by Maturity
 Higher proportion of longer maturity issuances
from 2009 and 2011 to compensate the low
rate / risk aversion (high level of capital
protection) environment
 Resilient number of short maturity issuances
since 2009
Source: BNP Paribas
10
5
28/11/2014
Implied dividend and financing rate
FOR INSTITUTIONAL INVESTORS WHO ARE ELIGIBLE CONTRACT PARTICIPANTS ONLY - NOT FOR USE WITH RETAIL INVESTORS
Current implied dividend term structure

145%
135%
S&P 500
125%
EuroStoxx50
Difference between S&P500 and EuroStoxx50
implied dividend term structure reflects the
flow of end users
 On S&P500, IBs have to sell delta (TR) and
buy back the dividends to hedge the puts they
sold to InsCo.
115%
105%
 Inversely on EuroStoxx IBs have to buy delta
95%
(TR) and sell back the dividends to hedge the
puts they bought through structured notes.
85%
75%
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
Source: BNP Paribas. Past performance is not indicative of future performance
7 year implied financing cost
7y TRS spread above Ibor3m
in % pts
1.2
1.0
S&P 500
0.8

EuroStoxx50
0.6
Difference between S&P500 and EuroStoxx50
on long term implied financing rate reflects
the flow end users.
0.4
 On EuroStoxx50 IBs have to carry the long
0.2
delta position on their balance sheet. Recent
change in regulation has made that more
expensive.
0.0
-0.2
 On S&P 500, InsCo synthetically provide a
-0.4
-0.6
Nov-11
flow of long term index performance.
May-12
Nov-12
May-13
Nov-13
May-14
Source: BNP Paribas. Past performance is not indicative of future performance
11
Fixed Annuity Sales over time
FOR INSTITUTIONAL INVESTORS WHO ARE ELIGIBLE CONTRACT PARTICIPANTS ONLY - NOT FOR USE WITH RETAIL INVESTORS
Over the last 10 years fixed annuity sales have been roughly stable. But Indexed Annuities
went from 16% to 60% of total Fixed Annuity sales
Fixed Annuity Sales per quarter over the last 10 years
35
30
Indexed
MVA
Fixed Book Value
$ Billions
25
20
15
10
5
0
2003 1Q 2004 1Q 2005 1Q 2006 1Q 2007 1Q 2008 1Q 2009 1Q 2010 1Q 2011 1Q 2012 1Q 2013 1Q 2014 1Q
Source: LIMRA.
12
6
28/11/2014
Indexed Annuity Market Overview
FOR INSTITUTIONAL INVESTORS WHO ARE ELIGIBLE CONTRACT PARTICIPANTS ONLY - NOT FOR USE WITH RETAIL INVESTORS

Indexed annuities now firmly established as the second largest annuity segment (beyond variable annuity, 3x FA market)

Sizeable issuance growth of 7.8% annual rate since 2007

Coming changes which could increase the FIA size:
 Low interest rates are driving a search for yield and players with alternatives strategies are becoming prominent
 Baby boomers nearing retirement are nearing peak age for IA sale
 Sales from 2004-05 (peak year before introduction of IA income rider) are about to exit the surrender charge period
US Indexed Annuity Issuance ($Bn)
Top 10 2Q2014 YTD Issuance Volumes ($Bn)
50
Allianz Life
Security Benefit Life
American Equity
GAFRI
Athene USA
Midland National Life
ING
EquiTrust
Symetra Financial
Fidelity & Guaranty Life
45
40
35
30
25
6,605
2,352
1,913
1,560
1,185
899
854
816
735
696
Top 10 2Q2014 Issuers Market Share
20
15
10
5
0
Source : BNP Paribas. Winks
*
13
Indexed & Indexed Universal Life market trends
FOR INSTITUTIONAL INVESTORS WHO ARE ELIGIBLE CONTRACT PARTICIPANTS ONLY - NOT FOR USE WITH RETAIL INVESTORS

2014 thus far one the most active year for new products with 22 new indexed annuities introduced
to the market by 6 carriers in Q2 alone

S&P 500 still the most common crediting allocation (51%) but down from 66% in Q2 2013. Hybrid
Indices are now 2nd at 31%.

Gradual increase toward hybrids exemplify market interest for innovative strategies delivering
uncapped crediting.

As of Q2 2014, 21 carriers offering indices beyond S&P 500 for indexed annuity and 27
carriers for Indexed Universal Life.

Annual point-to-point crediting represents 50% of sales. Monthly point-to-point distant 2nd at 16%

Majority of surrender periods at 10y ( 59%). But 7y surrender period sales increased to 16% from
5% in 2013.
14
7
28/11/2014
Indexed Annuity Most Popular Underlying
FOR INSTITUTIONAL INVESTORS WHO ARE ELIGIBLE CONTRACT PARTICIPANTS ONLY - NOT FOR USE WITH RETAIL INVESTORS

Increased competition encourages providers to look for differentiation

Security Benefit was the 1st to place FIA linked to custom indices
Custom Indices

International Indices

Euro Stoxx 50 – Allianz,
American General, ING

FTSE 100 – Allianz

Hang Seng – Aviva, Sagicor,
Midland National

iShares MSCI HK Index Fund
– Phoenix Life

Nikkei
225
American
General, National Western

3%
5%



Transparent Value Blended Index

Morgan Stanley Dynamic Allocation
Allianz

Athene
S&P Risk Control

Shiller Barclays CAPE Index

ForeThought

Fidelity & Guaranty Life

Symetra

American Equity


35%
Barclays US Dynamic Balance


DJIAAllianz,
American
Equity,
Athene,
Midland,
Phoenix Life
Russell
2000 – Allianz,
Ameritas, Midland, Sagicor
S&P Midcap 400 – Midland
Nasdaq 100 – Allianz, Midland,
Phoenix
Life,
Americo
Financial

Barclays ARMOUR II Gross USD 7% ER
DJ US Real Estate Risk Control 10%
S&P Average Daily Risk Control 5%
S&P Dividend Aristocrats 5% Risk Control
58%
0.46%
+Source:
Annuity linked Trader VIC Index


From 4Q 2013
to 2Q 2014
MSCI EAFE – Ameritas Life


72%
Non S&P Domestic

Security Benefit Life:
21%
S&P 500 is still the dominant index
with around 60% of the market
shares in the EIAs sold in the US
6%
BNP Paribas, Wink
S&P 500
US Domestic ex-S&P500
International
Custom indices
15
Volatility target overlay : methodology
FOR INSTITUTIONAL INVESTORS WHO ARE ELIGIBLE CONTRACT PARTICIPANTS ONLY - NOT FOR USE WITH RETAIL INVESTORS
To create a viable product for a fixed index annuity, new indices usually included a volatility target overlay.
A volatility target overlay adjust the exposure to the underlying assets with the aim of achieving a target volatility

The volatility control deleverages underlying exposure when volatility is rising and leverages when volatility is falling

Exposure not allocated to the underlying assets is allocated to a synthetic overnight cash
If realized volatility < Target
Increase underlying exposure
Set a target
level for
volatility
Overall volatility
~
Target volatility
Observe
underlying realized
volatility
If realized volatility > Target
Decrease underlying exposure
Exposure=
ோ௘௔௟௜௭௘ௗ ௏௢௟௔௧௜௟௜௧௬
்௔௥௚௘௧௏௢௟௔௧௜௟௜௧௬
Source: BNP Paribas, for illustration purposes.
16
8
28/11/2014
Why a Volatility Target overlay?
FOR INSTITUTIONAL INVESTORS WHO ARE ELIGIBLE CONTRACT PARTICIPANTS ONLY - NOT FOR USE WITH RETAIL INVESTORS
Advantages
Drawbacks
• Stable & predictable option price
with little dependency on market
conditions
• No extra premium paid for
implicit volatility
Efficient
Option
pricing
Innovation
• Expands the underlying assets
universe
• Trading an option is possible
even if the underlying index has
no or very illiquid volatility market
Offering
benefit
• Sales differentiation.
• Extension of the distribution
channel.
• Persistent product offering
• No cap on the crediting strategy
Correlation • Volatility target overlay can
between
have negative
index and
consequences in raging
its volatility
bull markets or in low
volatility bearish trends
Education
• IMOs & agents
• End client
Liquidity
• Option market is less liquid
than S&P 500’s option market
17
Volatility Target Overlay : Specification
FOR INSTITUTIONAL INVESTORS WHO ARE ELIGIBLE CONTRACT PARTICIPANTS ONLY - NOT FOR USE WITH RETAIL INVESTORS
Underlying
S&P 500
Target Volatility
15%
Observation Period
20bd and 60bd
Rebalancing Frequency
Daily
Maximum Exposure
200%
Tolerance
10%

Daily calculation of the annualized Realized Volatility of the Reference Index over the last 20 & 60 business days

Daily calculation of the Target Exposure :
Target Exposure 
Target Volatility
max( 20bd Volatility, 60bd Volatility)

Daily comparison of the Target Exposure to the Current Exposure

If the exposure is < 100%, the remainder of the portfolio is invested in cash

If the weight is > 100%, leverage is provided to the Underlying subject to the Maximum Leverage level

If the difference is > Tolerance, readjust to Target Exposure
28/11/2014
18
For Institutional Investors Only – Not For Use With Retail Investors, Not Intended for Further Distribution
9
28/11/2014
Hedging Volatility Target Overlay
FOR INSTITUTIONAL INVESTORS WHO ARE ELIGIBLE CONTRACT PARTICIPANTS ONLY - NOT FOR USE WITH RETAIL INVESTORS
Risk of simple delta hedging for option on indices with Volatility Target overlay :
 Volatility of volatility risk
 Jump risk.
 Underlying / Fixed income correlation risk
Realized volatility of S&P 500 with and without 15% volatility target overlay
200%
Exposure (rhs)
45%
60bd rlz vol
180%
40%
60bd rlz vol with overlay
160%
35%
140%
30%
120%
25%
100%
20%
80%
15%
60%
10%
40%
5%
20%
0%
Jan-02
Exposure
Volatility
50%
0%
Jan-03 Jan-04
Jan-05
Jan-06
Jan-07 Jan-08
Jan-09 Jan-10
Jan-11
Jan-12
Jan-13 Jan-14
Source: BNP Paribas, for illustration purpose.
19
Risks & considerations related to Notes investments
FOR INSTITUTIONAL INVESTORS WHO ARE ELIGIBLE CONTRACT PARTICIPANTS ONLY - NOT FOR USE WITH RETAIL INVESTORS
Purchasing the Notes involves a number of risks. Prospective investors should reach a purchase
decision only after careful consideration with their financial, legal, accounting, tax and other advisors
regarding the suitability of the Notes in light of their particular circumstances. The following highlight
some, but not all, of the risk factors in purchasing the Notes.

An investment in the notes is subject to the credit risk of the issuer. The actual or perceived
creditworthiness of the issuer may affect the market value of the Notes.

Certain built-in costs are likely to adversely affect the value of the Notes prior to maturity. The price, if
any, at which the issuer will be willing to purchase the Notes from investors in secondary market
transactions, if at all, will likely be lower than the original issue price and any sale prior to the maturity
date could result in a substantial loss.

The tax consequences of the Notes may be uncertain. You should consult your tax adviser regarding the
U.S. federal income tax consequences of an investment in the Notes.

The investor does not benefit from dividends of any securities comprising the underlying asset.

For Auto-Callable Features: The Note may be early redeemed one time at the sole discretion of the
Issuer and the investor will receive 100% of the invested principal plus an Early Redemption Premium.
No further payments will be made on the Notes.

For Knock-in puts: Risk of partial or total principal loss at maturity if the worst performing Index closes
below the Knock-Out Barrier on the Final Valuation Date. A knock-out Event, and thus Redemption
Amount at maturity, is not determined until the Final Valuation Date.
20
10
28/11/2014
Important Information
FOR INSTITUTIONAL INVESTORS WHO ARE ELIGIBLE CONTRACT PARTICIPANTS ONLY - NOT FOR USE WITH RETAIL INVESTORS
This material is for informational purposes only and is not intended to be a complete and full description of the discussion involved. Additional
information is available upon request. Neither the information nor any opinion contained in this material constitutes a recommendation,
solicitation or offer by BNP Paribas or its affiliates to buy or sell any security, futures contract, options contract, derivative instrument, financial
instrument, or service, nor shall it be deemed to provide investment, tax, legal, accounting or other advice. All opinions, information, and
estimates in this material constitute BNP Paribas’ or its affiliate’s judgment as of the date of this material. This material is only intended to
generate discussions regarding particular instruments and investments and is subject to change, or may be discontinued, without notice. This
material should neither be regarded as comprehensive nor sufficient for making decisions, nor should it be used in place of professional
advice. Information contained herein is derived from sources generally believed to be reliable, but no warranty is made that such information is
accurate, complete or fair and should not be relied on as such.
The risk of loss associated with futures and options trading, and trading in any other products discussed in this material, can be substantial.
Investors considering options trading may wish to review the Options Disclosure Document: Characteristics and Risks of Standardized Options
at (http://www.optionsclearing.com/publications/risks/riskchap1.jsp). The information on this website is not part of or incorporated by reference
in this document. The risk of loss in trading options and other derivatives can be substantial. Options involve risks and are not suitable for all
investors. This brief statement does not disclose all the risks and other significant aspects in connection with transactions of the type
described in this document
Neither BNP Paribas, persons connected with it, affiliates of BNP Paribas, nor any of their respective directors, partners, officers, employees or
representatives accepts any liability whatsoever for any direct or consequential loss arising from any use of these materials or their content.
The information in this material is not intended for distribution to, or use by, any person or entity in any jurisdiction where (a) the distribution or
use of such information would be contrary to law or regulations, or (b) BNP Paribas or a BNP Paribas affiliate would become subject to new or
additional legal or regulatory requirements.
BNP Paribas is incorporated in France with Limited Liability. Registered Office 16 boulevard des Italiens, 75009 Paris. BNP Paribas
Securities Corp., an affiliate of BNP Paribas, is a U.S. registered broker-dealer and a member of FINRA, the NYSE and other principal
exchanges.
© BNP Paribas, All Rights Reserved.
THIS DOCUMENT IS FOR THE GENERAL INFORMATION OF BNP PARIBAS’S CLIENTS AND IS A GENERAL SOLICITATION OF
DERIVATIVES BUSINESS FOR THE PURPOSES OF, AND TO THE EXTENT IT IS SUBJECT TO, §§ 1.71 AND 23.605 OF THE U.S.
COMMODITY EXCHANGE ACT.
21
11
VARIABLE ANNUITY (“VA”) PRODUCTS
PRICING CHALLENGES AND ISSUES
10th Annual Equity Based Insurance Guarantee Conference (Chicago)
Session 1B: November 17, 2014 (1045 – 1215 hours)
Amit Ayer
Executive Summary
Tactical challenges and issues with VA
pricing
Elements of VA Pricing Framework - Tactical
Principle
A
B
Description
Presence of Presence of living benefit rider places increased pressure on
living benefit adequate pricing framework
Risk-neutral
VA pricing framework is cumbersome because it must be
and real-world
performed using risk-neutral and real world frameworks
frameworks
Impact
Rider Reserves
Profitability Hedging
Charge / Capital










C
Multiple risk
Heterogeneity in risk management incentives necessitates VA
management
pricing functions to be agile and flexible
incentives

D
Accounting
Heterogeneity in accounting frameworks necessitates VA
asymmetries
pricing functions to be agile and flexible
on capital

E
Projection of
Reflecting hedging in pricing remains an arduous process with
hedging in
a range of practices
pricing

F
Pricing and
Even robust pricing of VAs cannot withstand prolonged
capital
markets with high realized volatility
markets
Significant impact to pricing



Insignificant impact to pricing

2
1
Executive Summary
Strategic challenges and issues with VA
pricing
Elements of VA Pricing Framework - Strategic
Principle
Description
Rider Reserves
Impact Charge / Capital Profitability Hedging
G
Pricing Lack of convergence around pricing metrics makes VA pricing
Metrics process more difficult


H
Scrutiny on
VA writers are able to meet pricing targets, but greater scrutiny
pricing
on pricing targets is warranted
targets


I
J
K
L
VA The deficiencies in VA disclosures should be further
disclosures investigated and improved

Volatility
Pricing of target volatility funds requires care and impact on
managed
statutory requirements should be further analyzed
funds

Exchange offers have demonstrated success across VA
VA Exchange
markets to eliminate liabilities from balance sheet through
offers
various offers
VA
Rejuvenated VA reinsurance market offers more flexibility with
reinsurance
regard to VA pricing frameworks
market
Significant impact to pricing








Insignificant impact to pricing
3
Agenda






Interdependencies between VA pricing components
Challenges and Issues with VA pricing
Lack of convergence around VA pricing metrics and anomalous
pricing results
Shortcomings with GAAP/IFRS ROE profitability metrics
Reactions to VA pricing difficulties
Questions and answers
4
2
INTERDEPENDENCIES BETWEEN VA
PRICING COMPONENTS
Interdependencies between VA pricing
components require an optimal risk-return
balance
Background



Traditional product pricing exercise searches for premium rate that meets desired profit target
Despite same fundamental objective for variable annuities, VA pricing exercise is much more
complicated because of presence of living benefit rider
Four elements of VA pricing process are intertwined and influence each other
Rider charge
•
•
Rider charge set with long-term view
Industry unlikely to factor to resetting GLB rider charge on
frequent basis
Reserves / Capital
•
•
Profitability affected by interest
earned on reserves / capital and
changes in reserves / capital levels
Many companies opt for stochastic
projections of reserves / capital
Hedging
•
Profitability
•
•
Sensitive to market conditions and
policyholder behavior
Warrants analysis of not just
expected value but also tail events
•
•
Hedging implemented to reduce
economic risk, and directly affects
the reserve / capital requirements
and profitability
Warrants analysis of not just
expected value but also tail events
Economic, GAAP and statutory
considerations
6
3
CHALLENGES AND ISSUES
WITH VA PRICING
Presence of living benefit rider places
increased pressure on adequate pricing
framework
A
B
C
Living benefit rider materially changes risk profile of base VA contract
Dimension
1
Profitability
2
Risk profile
VA Base Contract
Living Benefit Rider
5
6
Risk
Management
Contribution
to risk of
rider
• Roughly equivlavalent probability of upside • Increases risk of making large guaranteed payments,
loss and gain since base product passes
most of investment risk to policyholders
on separate • M&E fee only, with no volatility in separate
3 Dragaccount
value accounts
Reserve and
capital
requirements
F
investments
• No embedded derivatives
Policyholder
behavior
E
• Without the living benefit rider, profitability • Driven primarily by performance of underlying
relates to accruing fees that are adequate
to cover expenses and death claims
4
D
with low upside gain
• Embedded derivatives
• Increased drag on account value from rider fees and
potential downward movements in separate
accounts
• Minimal
• Living benefit rider contributes higher degree of
• Dependent on equity long term expected
• AG43 and C3 Phase-II requirements (while
uncertainty around policyholder behavior
volatilities for reserve and capital charges
• Typically not hedged
principles based) are higher in presence of living
benefit rider
• Cost of hedging (economic, GAAP, statutory, or
combination)
Significant impact to risk profile
Insignificant impact to risk profile
4
VA pricing framework is cumbersome
because it must be performed using riskneutral and real world frameworks (1/2)
A
B
Common approach to risk neutral pricing in VA pricing (illustrative)
180
160
PAD / Profit
4
120
Cost of Hedging
3
100
Real world pricing
(reserves / capital)
2
Basis Points (bps)
140
80
60
40
20
Risk Neutral Pricing /
“Economic Hedge
Cost”
1
0
VA product (rider fee and base contract)
1
C
D
E
F
Risk neutral valuation
• Almost all companies make a provision for the risk
neutral cost of the rider in pricing
• Business can be priced at today’s market conditions, the
product could be sold for up to two years
– Profitability results for business priced today are
by definition already stale tomorrow
Observations
•
•
Note – reflection of hedging in VA pricing is covered in Issue #5
Two primary approaches in generating risk-neutral
scenarios
– Use current market conditions to develop riskneutral scenarios to calculate rider cost
– Use long-term estimates to generate risk-neutral
scenarios to calculate the rider cost
Three methods used to set implied volatility for later
tenors
– Grade from longest-credible market-observable
tenor to target value
– Hold level at longest-credible market-observable
tenor
– Use level volatility throughout (e.g., long term9
estimates)
VA pricing framework is cumbersome
because it must be performed using riskneutral and real world frameworks (2/2)
A
B
Common approach to real-world pricing in VA pricing (illustrative)
180
160
PAD / Profit
4
120
Cost of Hedging
3
100
Real world pricing
(reserves / capital)
2
Basis Points (bps)
140
80
60
40
20
0
Risk Neutral Pricing /
“Economic Hedge
Cost”
1
VA product (rider fee and base contract)
2a
D
E
F
Real-world pricing
“Expense”
items
1
2
2b
Economic
Hedge Cost
Hedge
Effectiveness
Description
• Incorporated as an “expense” in base product pricing
through real-world scenarios
• Provision for hedge ineffectiveness by allowing portion
of real-world claims to flow through their integrated base
product and rider pricing
Reserves / Capital
1
Less refined
Description
• Develop set of factors that may vary on product
feature, duration, ITM
• Real-world scenarios to project reserve / capital
2
More refined
requirements
• Open considerations: number of scenarios, time
steps
3
4
Note – reflection of hedging in VA pricing is covered in Issue #5
C
Cost of hedging is calculated using a variety of methods,
implemented as en expense throughout base product
pricing (cannot lock in hedging costs today)
PADs are an additional buffer for deviations away from
10
expected loss (i.e., unexpected losses, which is
consistent with definition of capital)
5
Heterogeneity in risk management
incentives necessitates VA pricing
functions to be agile and flexible
• There are three primary views of living benefits products, yet
only one can be hedged at a time
– Fair value based (IFRS or economic)
– Statutory earnings and capital
– GAAP earnings
• The market sensitivities for the same product, captured by the
“Greeks,” are markedly different depending on the view taken
• Rho (interest sensitivity) is a significant driver of
the difference between the standards
A
B
C
D
E
Impact of behaviour assumptions on Delta
(illustrative)
Delta
Varying market sensitivity by accounting perspective
Illustrative policy with moderate investment risk
+/- 65%
+/3%
Point Estimate
liability Delta
Trading
strategy
tolerance
F
Behavioral
uncertainty
Observations
Sensitivity of measure
• While companies have made significant improvement
“Un-hedged”
economic
exposure under a
“GAAP SOP03-1”
program
Economics
“Theta”
Stat
GAAP FAS 133 GAAP SOP03-1
“Rho”
“Vega”
in managing policyholder behavior risk in recent years,
the amount of variability around a Delta point estimate
for an illustrative product is significant
• The interaction between pricing and hedging becomes
more difficult with variable annuities with the large
policyholder behavior uncertainty present in many of
the largest VA blocks today
• While C-suite at many companies have mentioned
policyholder behavior is under control for VA blocks, it
is interesting that many of these carriers are moving to
VAs without living benefits1
1
“Delta”
Lincoln Financial, Investor Day Sept 2014
11
Note: “Greek” estimates based on 10bps parallel change in interest rates, 1.0% change in implied volatility, 1.0% change in equit y levels
Heterogeneity in accounting frameworks
necessitates VA pricing functions to be agile
and flexible
A
Context / Background
D
E
F
+ Reduced
reported earnings
volatility
GAAP
- Under hedged
GMDB and GMIB
liabilities
- Increased
statutory capital
volatility
+ Replicates the “fair
value” of the embedded
guarantee
+ Allows for reasonable
future return on
policyholder behavior
assumptions
Economic
- Short-term volatility in
statutory capital /
reporting earnings
- Under-hedged to
shocks in equity
markets against
statutory capital model
- Cost/resource intensive
Biggest risk for three hedge approaches
+ Static downside
protection against
shocks to statutory
capital
+ Less costly than
economic hedge
Statutory
- Underhedged to rates
- Volatility in GAAP
earnings
- Hedge not economic
in less volatile
markets
- Tend to rely on longer
dated option markets
Shock Up
Advantages
Overview of Hedge Approaches
Disadvantages
C
A wide range of accounting and regulatory frameworks support a broad array of goals for
variable annuity hedging programs
Statutory
Shock Down

B
GAAP
Equity
markets
Economic
Interest
Rates
Shock Down
Shock Up
12
Source: Credit Suisse, 2012
6
Accounting differences for same product
can create pricing pressures due to
distortions in capital outcomes
A
Context



C
D
E
F
Two variable annuity contracts with same liabilities
One is accounted for using FAS 133
Other is accounted for using SOP 03-1
GAAP Liability value
Hypothetical GAAP liability under
interest rate stress scenario
Additional
liabilities in
stress scenario
Additional
capital
FAS
FAS
SOP
SOP
Insurer A


B
Insurer A
Insurer B
FAS 133: carrying at fair value
SOP 03-1: carrying at “least present value” of
cash flows
SOP 03-1 improves capital position


Insurer B
FAS 133: market interest rate assumption
SOP 03-1: “real world” interest rate
assumptions
SOP 03-1 reduces interest rate sensitivity
13
Reflecting hedging in pricing remains an
arduous process with a range of practices
Reflection of hedging in pricing models is not uniform across
insurers

A
B
C
D
E
F
Hedging approach generally impacted by a number of factors, including:
– Accuracy of results
– Complexity of implementation, validation, inputs and analysis
– Computational demands (software and hardware)
Range of
practices
Description
1
Reinsurance
• Assume that portion of claims along real world projection is reinsured
2
Change in
Liabilities
3
Proxy of hedge
transactions
4
Explicit projection
of hedging
Transactions
Sophistication
Accuracy
• Hedge payoff is some assumed percentage of change in liability
• Make Black-Scholes approximation on value of hedge assets along
real world scenario
• Full nested stochastic projection
14
7
Even robust pricing of VAs cannot
withstand prolonged markets with high
realized volatility


A
B
Disconnect exists between real world scenarios used to project
statutory balance sheet items and risk neutral scenarios used to project
change in market value of hedges
Higher implied volatility levels will increase the cost of hedging
associated with Delta hedging
C
D
E
F
Statutory requirements for GMAB policy – impact of dynamic hedging with index futures
Observations
Statutory requirement
140
•
120
•
100
80
60
40
AG43 requirement increases
with Delta hedging strategy,
requiring no required capital.
AG43 requirement with a
Delta hedging strategy
displays high sensitivity to
implied volatilities, as Delta
positions are sensitive to
changes in implied volatility.
20
0
Unhedged 10% vol
15% vol
AG43 Reserves
20% vol
25% vol
30% vol
35% vol
Required Capital
15
LACK OF CONVERGENCE AROUND
VA PRICING METRICS AND
ANOMALOUS PRICING RESULTS
8
Lack of convergence around pricing metrics
makes VA pricing process more difficult
G
General observations



H
I
J
K
L
Rider cost coverage, statutory IRR, and statutory VNB margins are the most prevalent
primary metrics
Statutory earning / capital strain and return on assets (ROA) are other significant metrics
While there is not one “correct” VA pricing metric, the variability of metrics across the industry
is expansive
Rider charge /
hedge cost
12
10
8
GAAP ROE typically
promulgated in
investor disclosures
6
4
2
0
Rider cost
coverage
Statutory IRR
on capital
Statutory VNB
Statutory
Market
margins
earnings/capital consistent VNB
strain
ROA
ROE
Statutory
capital/EAR
17
Source: Towers Watson 2013 Pricing Survey
VA writers are able to meet pricing targets,
but greater scrutiny on pricing targets is
warranted
GAAP/IFRS Pricing Targets and Minimums
G

20.0%
15.0%
10.0%

5.0%
0.0%
15th
Mean
85th
US GAAP/IFRS ROE - Target
US GAAP/IFRS ROE - minimum tolerable

Statutory Pricing Targets and Minimums

20.00%
15.00%
H
I
J
K
L
General observations
Companies reported significant
differences between pricing targets and
minimum tolerable levels
Companies pricing on a GAAP/IRFS
basis tended to have higher target ROEs
than overall industry statutory IRR figures
Companies are more apt to accept lower
hedge cost minimum due to low interest
rate environment
GAAP/IFRS ROEs are often inflated
(discussed during next section)
10.00%
5.00%
0.00%
15th
Mean
85th
Statutory IRR on Capital - target
Statutory IRR on Capital - minimum tolerable
18
Source: Towers Watson 2013 Pricing Survey
9
SHORTCOMINGS WITH GAAP/IFRS
ROE PROFITABILITY METRICS
Issues with GAAP VA general account
balance sheet
GAAP variable annuity general
account balance sheet
Commentary
1000
800
600
400
200
GAAP
value of
guarantee
fees
Net MV
hedge
assets
0
Assets
•
SOP 03-01
guarantees
DAC
Valuation
based on
non-market
assumption
is at odds
with an
economic
viewpoint
•
FAS 133
guarantees
Disconnected
from liability
valuation
H
I
J
K
L
• Inconsistent accounting treatment for
1200
Assumptions
locked in at
issue; relies
on mean
reversion
G
•
identical economic cash flows
• GMIB/GMDB vs. GMWB
• Rider vs. base contract
Inconsistent assumptions across different
companies
• DAC mean reversion assumptions
• Different assumptions around
application of FAS 157
Lack of transparency as to value and in
some cases the performance of the hedge
position
Little transparency around “sources of
earnings”
• Inconsistent definition of where results
appear (Net Income vs OCI)
Liabilities
20
10
Background on VA “opaqueness” around
variable annuity writers



G
H
I
The Insured Retirement Institute's study found a lack of disclosure
among annuity manufacturers around their investment hedging
strategies is depressing stock valuations
J
K
L
UBS Securities Managing Director Andrew Kligerman
–
“Investors want to know more about the effectiveness and cost of hedging, the
sensitivity of VA products to equity markets and interest rates, and where excess
capital is going”
–
“Clearly greater transparency is needed in the industry, which will improve investors’
perception as to the sectors’ strength and unlock capacity for growth”
Failing to meet investor expectations around transparency is largely due
to companies viewing their strategies as proprietary, thus providing full
transparency may place companies at competitive disadvantage
21
Articulation of “Opaqueness” Discount
• U.S. life insurers continue to trade at material discounts to
G
H
I
J
K
L
reported GAAP book value and exhibit low price / book ratios
compared to rest of the financial services industry
22
Source: Factset, company reports, UBS (6/30/12)
11
Major deficiencies in current VA disclosures
G
Deficiency
Description
• Provide little visibility into new business profitability
• Insurers do not communicate the underlying capital markets
1
Little transparency
into the value of
new business
2
Performance of the
business over time
cannot be tracked
many insurers have introduced modified earnings definitions that
leverage existing GAAP calculations
• Modified definitions are still rooted in GAAP concepts with all
attendant shortcomings.
3
Lack of credibility
of true tail risk
protection
• During the financial crisis, some insurers with large VA exposures
4
Disconnect between
economic
valuations and
GAAP and Statutory
frameworks
assumptions behind these stated ROEs
• In an attempt to foster greater understanding of their financial results,
suffered substantial capital calls forcing them to raise capital when
valuations were at a trough.
• Price to GAAP Book multiples for valuation benchmarks have
limitations
Inconsistent valuation of liabilities
Asset valuations geared to capital markets (not all DAC assets
are created equal)
–
–
H
I
J
K
L
Impact
• Full disclosures could help investors
understand that they have cheaper, and
certainly more transparent, vehicles to take
these types of capital markets positions
• Adjusted performance measures have
often left investors even more confused
and skeptical of companies’ assertions of
satisfactory VA performance
• Current disclosures do not provide
investors adequate assessment of whether
a given business is well protected against
such ill-timed capital calls going forward.
• The framework can leads to instances
where risk management decisions can
inappropriately influence reserve levels,
with the effect that reserves can increase
as a result of more hedging
Elements of better VA disclosures
In order to address these issues, a framework for better VA
disclosure needs to accomplish four things:
Area of Improvement
G
H
I
J
K
L
Description
• Portray new business profitability through objective metrics that can be compared across
1
Prove new business written is
profitable
2
Demonstrate the effectiveness of
hedging
• Allow the assessment of how much slippage has incurred between the hedge portfolio and the
3
Support valuation of the in force
business
• Provide pertinent information to investors to better assess the risk-adjusted economics of the in-
4
Investors’ dislike for VAs is
indiscriminate
companies and linked to overall capital efficiency goals
liability target, and how much unhedged positions have moved
force to inform valuations
• Establish that under harsh, yet conceivable, stress scenarios, the combination of hedging, reinsurance and other balance sheet resources is sufficient to protect key capital ratios
These disclosures should improve transparency to the financial
performance of VA blocks and enable investors to make informed
judgment on proper valuation and ultimately reduce the
opaqueness discount.
24
12
REACTIONS TO VA PRICING
DIFFICULTIES
Pricing of target volatility funds requires
care and impact on statutory requirements
should be further analyzed

G
H
Volatility managed funds and other new product features are now
common feature to reduce benefit cost and/or reduce challenges
associated with hedging
Dimension
1
2
3
Cost of
guarantee
Statutory
Hedging
Benefits of volatility managed funds
I
J
K
L
Considerations of volatility managed funds
• Potential for moderate reduction in economic losses
• Cost of guarantee lowered, but heavily dependent on
• Downside protected from fund rebalancing mechanisms, algorithm of volatility managed fund
preserving account value and lowering claims
• Minimal
• Impact on statutory requirements is minimal compared to
• Delta sensitivity is lowered markedly with volatility
• Material reduction in Vega exposure offers strong benefits
managed funds
impact on economics
from risk management standpoint
13
Case study parameters on volatility
managed funds
G
Modeling of rebalancing strategies
(static versus historical volatility)
 Static strategy
–
–
–
H
I
J
K
L
Model specifications
asset allocation between equity and bond is fixed
(75% equity 25% bond) at each time step
equity return >> bond return, sell equity and buy
bond
bond return >> equity return, purchase equity
and sell bond
 “Exponentially Weight Moving Average”
–
allocation between equity and bond depends on
historical annualized rolling portfolio volatility
– weighting of more recent returns more heavily
(exponentially weighted)
– at each time step, historical annualized volatility
is compared to a target volatility (15%)
 VIX
– Based on stochastic projection of VIX index
Interest rate
Two-factor Hull-White
Volatility
Heston
Projection period
40 years
Rebalancing frequency
Monthly
*Correlation between equity and interest rates
Product specifications
Issue age
55
Starting AV
$100,000
Rollup rate
3%
Rollup frequency
Monthly
Ratchet
Annually for 10 years
Withdrawal rate
5%
Contract fee
1.81%
MER fee
0.85%
27
Impact of volatility managed strategies on
Greek sensitivities
G
Vega changes most materially relative to a static strategy
$100
H
I
J
K
L
$80
$60
$40
$20
$0
-$20
-$40
Reserve Delta
Gamma
Rho
Vega
EWMA
Reserve Rho
VIX
Reserve Vega
Potential impact of target volatility strategies
Greek
Delta
Reserve Gamma
Static
•
•
•
•
•
•
•
•
Reduction in dollar Delta
Less transactions in equity futures, reducing transaction costs and cost of hedging
Reduction in dollar Gamma for VIX strategy
Mitigate hedge breakage during market volatility (Gamma or gap risk is typically not hedged)
Reduction in dollar Rho
Less transactions of interest rate swaps/Treasury futures in low interest rate environment
Significant reduction in Vega
Reduce need to enter into long dated puts or variance swaps
28
14
Exchange offers have demonstrated
success across VA markets to eliminate
liabilities from balance sheet through offers
G
H
I
J
K
L
Assessment of exchange offer target
Modern designs
Older designs
Size /
Homogeneity
Mitigating
adverse
selection
Variance in
economic vs
perceived
value
Overall
assessment
for offer
Commentary
target
Guaranteed Minimum
Death Benefit
•
•
Guaranteed Minimum
Accumulation Benefit
•
•
•
•
Guaranteed Minimum
Withdrawal Benefit
(non-lifetime)
•
•
Relatively homogeneous, typically return of premium
Lower perceived value than lifetime withdrawal benefit
Guaranteed Minimum
Income Benefit
•
Not homogeneous, as older regimes require election of annuitization not
present in modern designs
Target older regimes of income benefits that require election of
annuitization, as perceived value is likely lower compared to more recent
regimes
Guaranteed Minimum
Lifetime Withdrawal
Benefit
•
•
•
•
Subject to anti-selection if policyholders have non-uniform health status
Typically have low perceived value compared to economic value, in
particular for enhanced death benefits
Enhanced death benefits are excellent offer targets
Relatively homogeneous, with low expected lapse rates
Low perceived value compared to intrinsic value
Benefits that are past surrender charge and are close to expiry are
excellent offer targets
Variety of designs makes product heterogeneous
Highly lapse supported product makes mitigating adverse selection difficult
High economic values (rich features) and high perceived values (lifetime
income desired) make for poor offer targets
29
Significant characteristics across dimension
Some characteristics across dimension
Minimal characteristics across dimension
Rejuvenated VA reinsurance market offers
more flexibility with regard to VA pricing
frameworks
G
1
H
I
J
K
L
Markets
Description
Reinsurers
• Highly rated reinsurers are increasingly inquisitive about VA risks due to following reasons:
– Improve transparency around policyholder behavior frameworks
– Improved transparency around GAAP/IFRS profitability targets
– Emphasis on “sustainable products” with continued emphasis on volatility managed funds that afford policyholder
up-side
2
Banks
• Increased potential for diversification for reinsurers
• Most of the broader reinsurance market we have spoken with has expressed interest in variable annuity products
• Increased transparency being impetus for increased demand to take on risks in VA contracts
• Union Hamilton / Wells Fargo reinsurance deal for one of Lincoln’s largest living benefit rider started in 2013
• Banks have expressed similar inquisitions into taking on VA risks
The increased demand for VA reinsurance will certainly alter the VA
product development and / or pricing landscape
30
15
APPENDIX
The standard cash flows involved in a
variable annuity contract are depicted
below
Variable Annuity – key cash flow
diagram
Policyholder
Deposit
Advisor
Commission
(Initial +
Trailer)
Expenses
(e.g.,
Marketing,
Maintenance)
Insurance charge
General
Account
Rider charge
Initial
Commission
Trailer
Commission
Investment
Management
Fee
IMF = Investment
Management Fee
Separate
Account
Variable Annuity – key cash flow descriptions
Cashflow
Insurance
charge
Rider charge
Administrative Charges
Admin charge
Surrender Charge
IMF
Revenue
sharing
Investment manager
Surrender
charge
Description
• Paid by insurer to adviser at policy issue
• Typically ~ 5% of deposit
• Paid by insurer to adviser in Years 2+ while business is
inforce
• Typically 0.5% – 1%. May be a function of deposit or AV
• Fee charged for the management of the funds
in policy
• Typically 150 – 200 bps of AV
• Investment manager shares IMF with insurer
• Sometimes called M&E
• Typically 10 – 20 bps of AV
• Fee to support rider
• Ranges between 50 – 100 bps of GV
• Fee for contract admin; usually $30
• Paid by policyholder to insurer in the event of lapse (to
compensate for commissions paid)
• Typically 7 – 8%; declining by 1% pa
32
16
Elements of pricing framework will help
companies maintain profitability in variety of
economic scenarios
Robust VA pricing framework will help companies maintain profitability in
variety of economic scenarios
Dimension
1
GLB rider charge
2
Profitability of
contract
3
Reserve / Capital
requirements
4
Impact of hedging
5
VA reinsurance
market
Description
Solution
• Adequacy of GLB rider charge should be
addressed on an on-going basis
• Contract profitability should be analyzed
through stochastic scenarios with focus on
both mean and tail results
Focal
Point
• Risk neutral valuation
• Stochastic simulation, with focus across variety of
CTE(x%)
• Impact of reserve and capital requirements • Utilize inner loop functionality, using real world
should be integrated into profit analysis of
scenarios to determine capital adequacy
base contract
• Hedging impact should be analyzed in profit
analysis
• With increased demand for VA reinsurance
solutions, pricing frameworks should
understand landscape of reinsurers and
banks in market
• Variety of approaches available, with most
sophisticated being inner-loop nested stochastic
scenarios to simulate payoff of hedge assets
• Reinsurance can help reduce tail risk or diversify
existing VA block of business
Significant impact to pricing
Insignificant impact to pricing
33
Today, the most common type of guarantee
is the Guaranteed Minimum Withdrawal
Benefits (GMWB) and Income Benefit
(GMIB), which are similar
ROP GMWB – Policyholder taking regular withdrawals
Strong market performance can
increase account value…
…but persistent withdrawals
tend to reduce it over time
Withdrawals incur
guarantee claims
Withdrawals reduce account
value
Age /Time
Account value
ROP benefit
base
Policyholder withdrawal
from account value
Policyholder withdrawal
funded by insurer (i.e., claim)
Guarantee description



Insurer provides policyholder with a guaranteed withdrawal amount that never declines and
continues for life
Insurers incur guarantee claims equal to all withdrawals once the account value is
exhausted
Withdrawals can be fixed in number (e.g., 20 withdrawals permitted for 5% withdrawal rate)
or allowed for the lifetime of the insured
17
Consistency of VA guarantee valuation
A
Issue / context


B
C
D
E
F
Currently, FAS 133 is used for valuation of most Guaranteed Minimum
Withdrawal Benefits (GMWB) and Guaranteed Minimum Accumulation Benefits
(GMAB), while SOP 03-01 is used for all other GMxBs
– FAS 133 produces valuations more consistent with economics, larger in
magnitude and more sensitive to market changes
– SOP 03-1 produces valuations that are starkly different than the
economics, smaller in magnitude and less market-sensitive
Two VA contracts with highly similar risk and value characteristics could receive
very different values in the stress test
Outline of alternatives

This could create significant differences in stress test results for two insurers with
similar long-run risk and value profiles
Description
Status quo
Apply market consistent treatment for all VAs
• FAS 133 for valuation of most GMWBs and GMABs, SOP
• FAS 133 for all VAs
• Fees are not capitalized
03-1 for other GMxBs
• Fees are not capitalized
Pros (+) /
Cons (−)
+ Consistent with current GAAP accounting framework
– Inconsistent treatment of liabilities with similar risk profiles
– SOP 03-1 valuation is inconsistent with “economics”
+ Consistent accounting treatment of all VAs
+ Aligned with actual “economics”
– Requires additional work by supervised insurers
– Relies on unaudited financials
35
Prove new business written is profitable

G
H
Provide the product’s ROE if full capital markets hedging were
utilized (in other words an ROE that is not geared to capital
market risks, or “fully hedged” ROE), in addition to the ROE
using their own individual hedging strategy, across a number of
market scenarios
Interest Rate Scenario
Spread Scenario
June 2012 rates frozen for 5
Single A staying at 100 bps above
years then rising 50 bps per
Treasury
year across term spectrum
Single A staying at 100 bps above
June 2012 rates frozen until June Treasury
2014, then rising by 1% p.a. for
10 yrs, then frozen again
Single A widening to 150 bps above
Treasury by 2014
Interest rate term structure
Single A staying at 100 bps above
remains frozen at current 2012
Treasury
levels
Equity Scenario
0% p.a.
2% p.a.
4 % p.a.
0% p.a.
ROE – hedged as
planned
….
….
….
I
J
K
L
ROE – fully
hedged
….
….
2% p.a.
….
4 % p.a.
….
….
….
0% p.a.
2% p.a.
4 % p.a.
36
18
11/28/2014
Pricing Considerations
for VA and FIA/IUL
Products
Tim Hill, Principal, Milliman
10th Annual Equity Based Insurance Guarantees Conference (Chicago)
Session 1B: 17 November, 2014 (1045 – 1215 hours)
1
Fixed Indexed Annuities: Historical Sales
Historical US Annual Fixed Indexed Annuity
Premium (in billions)
50
$47
45
$39
$ billions
40
35
$30
30
$23
25
$27 $25
$25
$32 $32
$34
$27
20
15
$12
10
5
$3
$4
$5
$6
$14
$7
0
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
(ann)
YTD Q2 2014 FIA sales were $23,453 million, a 38% increase over YTD
Q2 2013.
2
Source: Wink, Inc.
1
11/28/2014
Fixed Indexed Annuities: Top Ten Companies
YTD Q2 2014
Rank
Company
YTD Q2 2014
(millions)
Market
Share
Change from
YTD Q2 2013
1
Allianz Life
$6,605
28.2%
175%
2
Security Benefit
$2,352
10.0%
13%
3
American Equity
$1,913
8.1%
-3%
4
Great American
$1,560
6.7%
43%
5
Athene USA
$1,185
5.1%
-11%
6
Midland National
$899
3.8%
11%
7
ING
$854
3.6%
65%
8
EquiTrust
$816
3.5%
-18%
9
Symetra
$735
3.1%
68%
10
Fidelity & Guaranty
$696
3.0%
41%
Source: Wink, Inc.
3
Q2 2014 FIA Sales by Channel
Q2 2014
•
The Agency Channel (independent
agents) dominates the FIA market, with
an 80% market share in Q2 14.
•
Compared to Q2 13, the Q2 14 market
share of the Bank channel increased
by 3.3 percentage points, to 11.1%, at
the expense of the Agency channel.
The B/D channel share also picked up
3 percentage points.
•
The bank channel experienced a
decline in market share from the
previous quarter, going from 13.5% in
Q1 14 to 11.1% in Q2 14.
•
Q2 13 market share:
Q2 2014 Premium: $12,566 million
Career
4%
B/D 5%
Bank
11%
Agency
80%
Agency: 87.3% B/D: 1.6%
Bank: 7.8%
4
Career: 3.3%
Source: Wink, Inc.
2
11/28/2014
Top Five FIA Sales Leaders by Channel
Q2 2014: Top Five Indexed Annuity Sales Leaders by Channel
Agency
Bank
Wirehouse
Career
1. Allianz
1. Great
American
1. ING
1. CNO
Companies
2. Security
Benefit
2. Allianz Life
2. Allianz Life
2. American
General
3. American
Equity
3. Protective
3. Nationwide
3. Nationwide
4. Athene USA 4. Lincoln
4. Protective
4. Horace Mann
5. Midland
5. Security
Benefit
5. Lincoln
5. Pacific Life
5
Bonus FIAs: Quarterly Market Share Trend
100%
81%
78%
75%
73%
64%
71% 71% 71% 69%
74% 76%
75% 75%
64%
70% 70%
77% 75%
75%
68% 67%
66% 66%
50%
25%
0%
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2
09 09 09 09 10 10 10 10 11 11 11 11 12 12 12 12 13 13 13 13 14 14
In Q2 14, 66% of fixed indexed annuity sales offered a premium bonus, compared to
68% in Q2 13. Premium bonuses on the account value ranged from 0.50% to 12% in Q2 14.
Source: Wink, Inc.
6
3
11/28/2014
Fixed Indexed Annuity Trends
 Sales by Surrender Period: In Q2 14, 59% of FIA policies were sold
with a 10-year surrender charge schedule
– 7-yr schedule increased from 4.7% of FIA policies sold a year ago to
16.2% in Q2 2014.
 Average Issue Age: The average issue age reported for Q2 14 FIA
sales was age 63.
 Average Premium: The Q2 2014 average FIA premium was
$82,114, representing a 1% increase from the previous quarter, while
it ranged from $17,363 to $125,272.
 Guaranteed Lifetime Withdrawal Benefits (GLWB): Approximately
63% of all FIAs offer a GLWB (Q2 13: 72%). Approximately 29 FIA
companies offer GLWBs.
– Roll-ups range from 2% to 14%, offered on both a compound and simple
interest basis.
– The average GLWB rider charge is 0.84%.
7
Source: Wink, Inc.
Fixed Indexed Annuity Sales through Banks
 Q2 2014 Fixed Indexed Annuity sales through the Bank channel reached
$1.72 billion, just below the recent record high of $1.77 billion set in Q4 13,
and 1% behind Q1 2014. Compared to Q2 2013, volume has increased
62%.
 The FIA share of total bank fixed annuity premium in Q2 2014 was 32.5%,
compared to 31.6% in Q1 2014 and 33.1% in Q2 2013.
 After five consecutive quarters of increasing market share, the bank share of
total industry FIA sales declined to 13.2%, compared to the high of 15.4% in
Q1 2014. (Reported by BISRA; numbers reported by Winks differ, although
the decline from Q1 14 to Q2 14 is also noted by Winks.)
8
Source: BISRA
4
11/28/2014
Fixed Indexed Annuities: Top Ten Companies
selling through the Bank Channel – Q2 2014
Rank
Company
Q2 2014 Sales
(in millions)
1
Great American
$364
2
Symetra Financial
$337
3
Allianz Life
$334
4
Protective Life
$140
5
Lincoln Financial
$125
6
Pacific Life
$102
7
ING
$101
8
AIG
$96
9
Jackson National
$30
10
Genworth Financial
$28
Source: BISRA
9
What Does a Bank FIA Look Like
 Surrender Charge
 5 or 7 year surrender schedule: 9, 8, 7, 6, 5% or 9%,8,7,6,5,4,3
 10% free withdrawal provision
 May have return of premium
 Minimum surrender value: 100% at 1% minus SC
 Interest Crediting Accounts
 Fixed Account
 Some indexed accounts based on S&P 500
 Perhaps an alternative such as bond index or something else
 Commission
 3.0% to 4.0% commission for 5-year product, 4.0% to 5.0% of premium for 7year
 Extended care waiver, terminal illness waiver
 Optional riders
 GLWB Rider – 0.75% to 0.95% annual charge (of benefit base), 6 – 8% roll-up
credit, 10 to 20-yr income roll-up period. Attained Age based payout factors with
5.0% at age 65.
10
5
11/28/2014
Important Pricing Issues
 Yield Curve environment / duration calculation
 Option Payoff assumption
 Base Lapse / Dynamic Lapse (both interest sensitive and
GLWB impact)
 Policyholder utilization of GLWB
 Changing nature of products
 Upfront bonus may not be a must have
 "Stacking" GLWBs
 Actuarial Guideline 33
 Annuity 2012 table
11
Indexed Life: Historical Sales
Historical US Annual Indexed Life Premium
(in millions)
2,000
$1,803
1,800
1,600
$1,356$1,392
1,400
1,200
$973
$ millions
1,000
800
$696
$512 $539 $531
600
$352
400
200
$64
$62
$63
$84
$186
$99 $101 $139
0
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
(ann)
YTD Q2 2014 Indexed Life sales were $697 million, a 9% increase over
YTD Q2 2013.
12 Source: Wink, Inc.
6
11/28/2014
Indexed Life: Top Ten Companies YTD Q2 2014
Rank
13
Company
YTD Q2 2014
(millions)
Market
Share
Change from
YTD Q2 2013
1
Pacific Life Cos.
$86.0
12.3%
-0.6%
2
Aegon
$64.7
9.3%
41%
3
National Life Group
$61.3
8.8%
25%
4
Minnesota Life
$48.6
7.0%
30%
5
Allianz Life
$33.8
4.9%
23%
6
Midland National
$33.5
4.8%
52%
7
Zurich Life
$33.0
4.7%
9%
8
Lincoln National
$31.0
4.4%
62%
9
Nationwide
$30.8
4.4%
46%
10
AXA US
$27.6
4.0%
-35%
Source: Wink, Inc.
Q2 2014 Indexed Life Sales by Channel
Q2 2014
•
The Agency Channel (independent
agents) is the dominant channel in the
indexed life market, with a 65% market
share in Q2 14.
•
Compared to Q2 13, the Bank and
Broker/Dealer channels’ market share
picked up slightly, mostly at the
expense of the Career channel.
•
Q2 13 market share:
Q2 2014 Premium: $367 million
Career
19%
Agency
65%
B/D
14%
Agency: 65.7% B/D: 12.5%
Bank: 0.8%
Career: 21.0%
Bank
1%
14
Source: Wink, Inc.
7
11/28/2014
Top Five Indexed Life Sales Leaders by
Channel
Q2 2014: Top Five Indexed Life Sales Leaders by Channel
Agency
Bank
Wirehouse
(B/D)
Career
1. Pacific Life
1. Pacific Life
1. Aegon
1. AXA US
2. Midland
National
2. National
Western
2. Nationwide
2. CNO Group
3. National Life 3. Nationwide
3. Minnesota
Life
3. National Life
4. Allianz
4. Lincoln
4. Pacific Life
4. RiverSource
5. Minnesota
Life
5. American
National
5. Lincoln
5. Minnesota
Life
15
Indexed Life Sales Trends
 Sales by Surrender Period: In Q2 14, 51% of Indexed Life policies
were sold with a 9- to10-year surrender charge schedule. The next
most popular surrender period was 15 years, garnering a 33% market
share.
 Average Issue Age: The average issue age reported for Q2 14
Indexed Life sales was age 42. Average issue age ranged from 31 to
52 years old.
 Face Amount/Policy Count: The average face amount in Q2 14
was $428,904, with individual carrier results ranging from $83,292 to
$1,471,835. The overall policy count for Q2 14 was 81,889 policies.
 Average Indexed UL Target Premium: The Q2 2014 average
Indexed UL target premium was $6,367, representing a 30%
decrease from the previous quarter, while it ranged from $1,110 to
$17,538
16
Source: Wink, Inc.
8
11/28/2014
Important Pricing Issues for IUL
 Policyholder behavior
 Persistency and premium persistency
 Option Payoff assumption
 Illustrations
 Simplified Underwriting
 Use of loans and rate charged on loans
 Accelerated death benefits
17
9
11/28/2014
Risk Managed Funds
10th Annual Equity Based Insurance Guarantees Conference (Chicago)
Session 2A
1330 – 1500 hours
Marshall C. Greenbaum, CFA, ASA
AnchorPath Financial, LLC
November 17, 2014
For Society of Actuaries 2014 Equity Based Insurance Guarantees Conference Use Only — Not For Public Viewing or Distribution
This information is for discussion purposes only. See Important Disclosures in the document.
Disclaimer
Past performance is not a guarantee of future performance. Investment returns and principal values will fluctuate, so an investor's account value may be worth more or less
than their original deposits. Recipients must make their own independent decisions regarding any strategies, securities, investments or financial instruments mentioned herein.
AnchorPath uses derivatives, such as options and interest rate futures, the effect of which is included in the performance.
AnchorPath Financial, LLC (“AnchorPath”) is an SEC registered investment adviser. The client is referred to Form ADV Part 2A for more information regarding AnchorPath. SEC
registration does not constitute an endorsement of the firm by the Commission nor does it indicate that the adviser has attained a particular level of skill or ability.
This document does not constitute an offer, invitation or recommendation to subscribe for or purchase any services, security or financial product. This document is being
circulated on a restricted and confidential basis, is solely for the purpose of providing information about AnchorPath and its strategies and is not, and should not be construed as,
a recommendation of AnchorPath. AnchorPath does not make any representations that the products or services are suitable or appropriate for the recipient. The information in
this document is confidential and must not be reproduced or disclosed, in whole or in part, to any other party without the prior consent, in writing, from AnchorPath.
In preparing this document, AnchorPath has relied on information which is publicly available and sources believed to be reliable. This information has not been independently
verified by AnchorPath. This document does not purport to contain all of the information that the recipient may require to evaluate any investment strategy and does not take into
account the investment objectives, financial situation or particular needs of the recipient. Each recipient should conduct its own independent investigation and assessment (and is
responsible for its own costs in so doing) of the contents of this document and of the economic, financial, ERISA, regulatory, legal, taxation and accounting implications for the
recipient. Each recipient acknowledges that it is not relying on this document in considering the merits of any particular transaction.
Except as required by law, AnchorPath and its respective directors, officers, employees, agents and consultants make no representation or warranty as to the accuracy,
completeness, timeliness, fairness or reliability of the information in this document, and accept no liability under any circumstances for any loss or damage whatsoever arising as
a result of any omission, inadequacy, or inaccuracy in this document or otherwise arising in connection with it.
This document may contain certain forward-looking statements, forecasts, estimates, projections and opinions (“Forward-Looking Statements”). No representation is made or will
be made that any Forward-Looking Statements will be achieved or will prove to be correct. Actual future results and operations could vary materially from the Forward-Looking
Statements. Each recipient acknowledges that circumstances may change as a result of many events or factors, not all of which are known to AnchorPath or within its control,
and the contents of this document may become outdated as a result.
The AnchorPath strategy involves significant risks due to, among other things, the nature of the investments utilized in the AnchorPath strategy. Any discussion of risks contained
herein with respect to any product or service should not be considered to be a disclosure of all risks or a complete discussion of risk involved. In considering information
contained in this document, recipients should bear in mind that there can be no assurance that the AnchorPath strategy will be able to implement its investment strategy and
investment approach or achieve its investment objective.
For Society of Actuaries 2014 Equity Based Insurance Guarantees Conference Use Only — Not For Public Viewing or Distribution
This information is for discussion purposes only. See Important Disclosures in the document.
2
1
11/28/2014
Agenda

Risk Managed Fund Terminology

Advantages of Risk Managed Funds

Risk Managed Fund History and Evolution

Review 3 Risk Management Strategies:


Constant proportional portfolio insurance (CPPI)

Risk parity

Volatility control
Emerging Approaches


Example:
Ohio National Risk Managed Balanced
sub-advised by Janus & AnchorPath
Q&A
For Society of Actuaries 2014 Equity Based Insurance Guarantees Conference Use Only — Not For Public Viewing or Distribution
This information is for discussion purposes only. See Important Disclosures in the document.
3
Risk Managed Fund Terminology

Risk Management is not synonymous with Risk Monitoring

Numerous types of approach and terms in the marketplace

Generic: risk-managed, managed-risk, systematic, rules-based

Volatility based: risk control, managed volatility, target volatility, volatility control

Allocation based: dynamic allocation, multi-strategy, multi-asset, risk parity, risk
balancing

Option based: portfolio insurance, constant proportional portfolio insurance (CPPI),
capital protection strategy, collar, floor-leverage

Other: sector rotation, high watermark, tail-risk, smart beta, managed payout, exotic
beta, momentum, liquid alts, derivatives overlay, risk management overlay, …
For Society of Actuaries 2014 Equity Based Insurance Guarantees Conference Use Only — Not For Public Viewing or Distribution
This information is for discussion purposes only. See Important Disclosures in the document.
4
2
11/28/2014
Risk Managed Fund Main Concept

Most risk managed funds employ some form of Dynamic Allocation
between risky and non-risky assets

Many attempt to forecast risk as the basis for reducing equity exposure

Some use absolute fund performance to determine equity allocation

Many only rely on rebalancing asset classes within broad ranges

Some use financial options (e.g. put contracts)
For Society of Actuaries 2014 Equity Based Insurance Guarantees Conference Use Only — Not For Public Viewing or Distribution
This information is for discussion purposes only. See Important Disclosures in the document.
5
Risk Managed Fund Benefits for VAs

Risk managed funds can enhance VA products in multiple ways

By reducing


rider benefit guarantee costs

hedge cost to the insurer

reserves and capital charges
By stabilizing

ongoing reserves and capital charges

M&E fee revenue

For the policyholder its allows for surrender of a policy when markets are down
without potentially forfeiting significant value of the guarantee benefit rider

Allows for products without explicit guarantees while still providing valuable risk
management to policyholders
For Society of Actuaries 2014 Equity Based Insurance Guarantees Conference Use Only — Not For Public Viewing or Distribution
This information is for discussion purposes only. See Important Disclosures in the document.
6
3
11/28/2014
History of Key Risk Managed Strategies
1973:
1976:
1980:
1982:
1986:
Black Scholes option pricing model
Portfolio Insurance developed
Portfolio Insurance implemented
S&P 500 index futures contract introduced
$60b+ managed using Portfolio Insurance
Constant Proportional Portfolio Insurance (CPPI) developed
Market crash discredits Portfolio Insurance because of futures market dislocation
Risk-parity developed
Listed & OTC Options markets grow
Long Term Capital demise; period of high volatility
Principal Protected Funds launched
Internet bubble
Financial crisis through 2009
Principal protected, target-date, managed payout funds discredited by the financial crisis
Risk/volatility control indices launched
Volatility control poor performance scrutiny
Risk-parity poor performance scrutiny
Prevalence of risk managed funds/offerings in the marketplace
1987:
1996:
1997:
1998:
1999:
2002:
2008:
2009:
2011:
2013:
2014:
For Society of Actuaries 2014 Equity Based Insurance Guarantees Conference Use Only — Not For Public Viewing or Distribution
This information is for discussion purposes only. See Important Disclosures in the document.
7
Risk Managed Strategies: CPPI

CPPI Payoff is path-dependent

Set Floor Level


Typically an amount to provide principal protection at a horizon date

Zero coupon bond Reference curve can be used

Distance = Portfolio Value less Floor Level
Set a “Cushion” rate


Typically set such that risky asset portfolio expected not to drop before rebalancing
opportunity, example 20%
Risky asset allocation = Distance / Cushion

At inception with 90% floor; risky allocation = (100% - 90%) / 20% = 50%

Remainder invested in fixed interest assets

Adjusts allocation to risky assets as portfolio changes value
For Society of Actuaries 2014 Equity Based Insurance Guarantees Conference Use Only — Not For Public Viewing or Distribution
This information is for discussion purposes only. See Important Disclosures in the document.
8
4
11/28/2014
Risk Managed Strategies: CPPI
Portfolio Value
CPPI Illustration
Portfolio with variable
allocation between
Risky Assets & Fixed
Interest Assets
100%
Distance relative to
Portfolio Value
Zero Coupon Bond
Reference Curve
0%
Maturity
Time
For Society of Actuaries 2014 Equity Based Insurance Guarantees Conference Use Only — Not For Public Viewing or Distribution
This information is for discussion purposes only. See Important Disclosures in the document.
9
Risk Managed Strategies: CPPI


Pros

Relatively simple

Easily implemented

Provides floor protection
Cons

Equity allocation can go to zero and remain there

Good early performance drives returns in many good performing scenarios

Objective of principal protection at single horizon is not ideal for most investors

Exposed to “gap” (i.e. market crash) risk
For Society of Actuaries 2014 Equity Based Insurance Guarantees Conference Use Only — Not For Public Viewing or Distribution
This information is for discussion purposes only. See Important Disclosures in the document.
10
5
11/28/2014
Risk Managed Strategies: Risk Parity

Diversify risk and return by equalizing asset class volatility

Leverage resulting portfolio to desired level of risk
Two asset class example:
Portfolio
Volatility
Equity
15%
Bond
5%
Correlation
Traditional
Equal Risk
Equal Risk
2x Leverage
60%
25%
50%
-0.35
40%
75%
150%
Total Exposure
100%
100%
200%
Expected Volatility
8.5%
4.25%
8.5%
For Society of Actuaries 2014 Equity Based Insurance Guarantees Conference Use Only — Not For Public Viewing or Distribution
This information is for discussion purposes only. See Important Disclosures in the document.
11
Risk Managed Strategies: Risk Parity
Expected Return
Risk Parity
Portfolio (levered)
Tangent
Portfolio
Risk Parity
Portfolio (un-levered)
Expected Standard Deviation
For Society of Actuaries 2014 Equity Based Insurance Guarantees Conference Use Only — Not For Public Viewing or Distribution
This information is for discussion purposes only. See Important Disclosures in the document.
12
6
11/28/2014
Risk Managed Strategies: Risk Parity


Pros

Worked well in many market cycles

Easily implemented

Greater emphasis on real assets/commodities for potential diversification
Cons

Short term performance will be poor in quickly rising interest rate scenario

Low interest rate environment can make entry point important

May not protect against systemic allocation shift from risky assets to cash

No explicit downside protection
For Society of Actuaries 2014 Equity Based Insurance Guarantees Conference Use Only — Not For Public Viewing or Distribution
This information is for discussion purposes only. See Important Disclosures in the document.
13
Risk Managed Strategies: Volatility Control

Seeks to achieve a target volatility of portfolio returns

Generally use recent (typically 60-90 days) backward-looking
calculation to predict current volatility

Scale equity allocation based on the volatility prediction

Equity Allocation = Target Volatility / Predicted Volatility

Remainder invested in cash

Equity allocation capped at 150% for S&P Daily Risk Control 10%
Index
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This information is for discussion purposes only. See Important Disclosures in the document.
14
7
11/28/2014
Risk Managed Strategies: Volatility Control
S&P 500 Index and realized volatility since 2008
2008
150
2009
2010
2011
2012
2013
2014
Financial crisis and
summer 2011 are notable
recent periods with
significant equity
drawdowns and
escalated volatility levels
S&P 500 USD Total Return Index ( 6/3/2008 = 100 )
S&P 500 Daily Risk Control 10% USD Total Return Index ( 6/3/2008 = 100 )
S&P 500 Daily Risk Control 10% USD Total Return Index ( 1/1/2011 = 97.55 )
125
100
50
125%
Equity Allocation
Cash Allocation
Risk Controlled Index
SPTR Index
80%
Allocation
100%
60%
75%
40%
50%
20%
25%
0%
Volatility (60 day EMAvg)
75
0%
Sources: AnchorPath, Bloomberg, Dow Jones & Co
See Important Disclosures
For Society of Actuaries 2014 Equity Based Insurance Guarantees Conference Use Only — Not For Public Viewing or Distribution
This information is for discussion purposes only. See Important Disclosures in the document.
15
Risk Managed Strategies: Volatility Control
Volatility has been controlled but protection has been inconsistent
Realized volatility of the
risk control index has
been relatively stable,
with much less variation
than the S&P 500 Index
In 2008, the risk control
index avoided a large
drawdown in comparison
to the S&P 500
In 2011, the risk control
index did not provide
downside protection from
the equity market
downturn
Sources: AnchorPath, Bloomberg, Dow Jones & Co
See Important Disclosures
For Society of Actuaries 2014 Equity Based Insurance Guarantees Conference Use Only — Not For Public Viewing or Distribution
This information is for discussion purposes only. See Important Disclosures in the document.
16
8
11/28/2014
Risk Managed Strategies: Volatility Control
Risk control benefit is highly dependent on entry point
Risk controlled index
performance has
significantly lagged the
S&P 500 since 2011
The risk control index
cannot avoid drawdowns
when volatility increases
abruptly
Sources: AnchorPath, Bloomberg, Dow Jones & Co
See Important Disclosures
For Society of Actuaries 2014 Equity Based Insurance Guarantees Conference Use Only — Not For Public Viewing or Distribution
This information is for discussion purposes only. See Important Disclosures in the document.
17
Risk Managed Strategies: Volatility Control


Pros

Easily implemented

Can reduce/stabilize cost of options compared to uncontrolled index

Can provide downside protection
Cons

May not work in sudden market moves, lag can cause negative skew

Possibility of large allocation to cash

Investors seek good returns, not simply stable volatility

Poor recent performance
For Society of Actuaries 2014 Equity Based Insurance Guarantees Conference Use Only — Not For Public Viewing or Distribution
This information is for discussion purposes only. See Important Disclosures in the document.
18
9
11/28/2014
Emerging Integrated Approaches

Learn from the past and improve on prior approaches

Integrate multiple strategies

Avoid “gap” risk

Invest in volatility rather than control it

Adapt market risk/derivatives management techniques employed
by investment banks and insurance companies

Pursue more robust investor objective than simple principal
protection
For Society of Actuaries 2014 Equity Based Insurance Guarantees Conference Use Only — Not For Public Viewing or Distribution
This information is for discussion purposes only. See Important Disclosures in the document.
19
Integrated Risk Management In Practice
Fund Example: Ohio National Risk Managed Balanced (RMB)
for illustration purposes only
RMB fund strategy
integrates multiple risk
management approaches
Peers* encompass risk
managed funds following
strategies such as
volatility control, risk
parity, low vol, tactical
allocation, etc.
Risk/Return Metrics
(5/1/14-10/31/14)
RMB
Fund Peer
Minimum
Fund Peer
Maximum
5.8%
-2.3%
7.8%
11.8%
-4.5%
16.0%
Volatility (annualized)
5.4%
4.6%
9.4%
Sharpe Ratio
2.1
Return (cumulative)
Return (annualized)
Max Drawdown
-3.3%
-0.8
-8.8%
For the period, RMB fund
had the highest Sharpe
ratio among peers
1.8
-4.1%
*As
of October 31, 2014. Performance based on daily returns of funds since RMB inception May 1, 2014. Peer Group comprises all
funds in the Managed Volatility Portfolios (MVPs) category that are available in Ohio National's individual and group variable annuity
products
Source: AnchorPath, Morningstar
Past performance is not a guarantee of future performance
See Important Disclosures
For Society of Actuaries 2014 Equity Based Insurance Guarantees Conference Use Only — Not For Public Viewing or Distribution
This information is for discussion purposes only. See Important Disclosures in the document.
20
10
11/28/2014
Integrated Risk Management In Practice
Fund Example: Ohio National Risk Managed Balanced (RMB)
for illustration purposes only
The fund is managed in
two components
Balanced Component is
managed by Janus
Risk Management
Component (RMC) is
managed by AnchorPath
Risk/Return Metrics
(5/1/14-10/31/14)
RMB
Balanced Component
5.8%
4.1%
11.8%
8.3%
Volatility (annualized)
5.4%
6.0%
Sharpe Ratio
2.1
1.4
-3.3%
-4.3%
Return (cumulative)
Return (annualized)
Max Drawdown
*As
Since inception, the RMC
has enhanced return,
reduced volatility and
limited drawdown
compared to a straight
investment in the
Balanced Component*
of October 31, 2014
Source: AnchorPath, Morningstar
Past performance is not a guarantee of future performance
See Important Disclosures
For Society of Actuaries 2014 Equity Based Insurance Guarantees Conference Use Only — Not For Public Viewing or Distribution
This information is for discussion purposes only. See Important Disclosures in the document.
21
Integrated Risk Management In Practice
Performance surrounding July, 31 2014 when S&P 500 dropped 2% in one day
for illustration purposes only
Period: 7/8/14 – 8/8/14
Bal. Comp.
- 1.0%
RMC Contr.
+0.9%
RMB fund
-0.1%
The gain in the RMC
almost exactly offset the
decline in the Balanced
Component
Risk/Return Metrics
(5/1/14-10/31/14)
RMB
Balanced Component
5.8%
4.1%
11.8%
8.3%
Volatility (annualized)
5.4%
6.0%
Sharpe Ratio
2.1
1.4
-3.3%
-4.3%
Return (cumulative)
Return (annualized)
Max Drawdown
*As
Multiple risk management
techniques contributed to
the offset over the period
of October 31, 2014
Source: AnchorPath, Morningstar
Past performance is not a guarantee of future performance
See Important Disclosures
For Society of Actuaries 2014 Equity Based Insurance Guarantees Conference Use Only — Not For Public Viewing or Distribution
This information is for discussion purposes only. See Important Disclosures in the document.
22
11
11/28/2014
Integrated Risk Management In Practice
Performance during October 2014 market volatility
for illustration purposes only
The interplay of multiple
techniques cushioned the
drawdown at October
15th low
Risk/Return Metrics
(5/1/14-10/31/14)
RMB
Balanced Component
5.8%
4.1%
11.8%
8.3%
Volatility (annualized)
5.4%
6.0%
Sharpe Ratio
2.1
1.4
-3.3%
-4.3%
Return (cumulative)
Return (annualized)
Max Drawdown
*As
RMB fund performance
demonstrates that a
next-gen approach can
add significant value
of October 31, 2014
Source: AnchorPath, Morningstar
Past performance is not a guarantee of future performance
See Important Disclosures
For Society of Actuaries 2014 Equity Based Insurance Guarantees Conference Use Only — Not For Public Viewing or Distribution
This information is for discussion purposes only. See Important Disclosures in the document.
23
Concluding Remarks

Risk managed funds provide value to policyholders and insurers

Diversity of risk managed fund strategies is growing

Mandatory allocation to risk managed funds benefits policyholders and
carriers

Many being used in “investment only” VAs as a risk management
feature

Each market drop provides opportunity to differentiate managers and
showcase the benefit of risk managed strategies
For Society of Actuaries 2014 Equity Based Insurance Guarantees Conference Use Only — Not For Public Viewing or Distribution
This information is for discussion purposes only. See Important Disclosures in the document.
24
12
11/28/2014
Contact
Marshall Greenbaum, CFA, ASA
AnchorPath Financial, LLC
1266 East Main Street, Suite 700R
Stamford, CT 06902
203-893-3600
[email protected]
For Society of Actuaries 2014 Equity Based Insurance Guarantees Conference Use Only — Not For Public Viewing or Distribution
This information is for discussion purposes only. See Important Disclosures in the document.
25
13
28/11/2014
Volatility Control – Optimising the
overall product framework?
Product providers are being asked to provide attractive guaranteed products within increasing risk
management and capital constraints. Volatility control investments combined with capital protection
can be viewed as an effective technique to help balance the requirements of all stakeholders:
carriers, policyholders, markets and regulators.
Stephen Einchcomb, RBS
10th Annual Equity Based Insurance Guarantee Conference (Chicago)
Session 2A
17 November 2014 (1330 – 1500 hours)
Building an Insurance Product – balancing the requirements of stakeholders
Policyholders
•
•
•
Lifestyle Insurance
Growth and protection
Other tax-efficient savings
Distribution
Regulators
£
•
•
•
•
•
•
•
Preferred sales channel
Advisor education
Customer behaviour
Insurance Product
Financial Markets
•
•
•
Accounting
Capital requirements
Tax treatment
Consumer Protection
Carrier
•
•
•
Low rates
Volatility management
Re-insurance
Risk and asset management
Capital management
Return on equity
Product design influenced by factors external to the product provider
Above charts are for illustrative purposes only.
RBS00000
2
1
28/11/2014
Policyholders
Lifestyle Insurance
Desire for growth and protection due to loss aversion
The Insurance Sector
Overview of the two main archetypes of insurance
Life / Pension Insurance
£
Non-Life (Property & Casualty Insurance)
£
Insurance is the transfer of risk from policyholder to insurer in exchange for a payment, the pooling of risk resulting in less
uncertainty due to law of large numbers and the use of diversification across geography and across product lines
Source: RBS, October 2014
4
RBS00000
Above charts are for illustrative purposes only.
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28/11/2014
Life Insurance or Lifestyle Insurance
Life insurance will fill the void left by declining Defined Benefit pension provision
Defined Benefit
Defined Contribution
•
Allowed individuals to have certainty and plan for
retirement (e.g. 67% of final salary at 67).
•
Focus on tax-deferred accumulation not pension outcome
•
Limited risk management
•
Focus is on individual’s outcome.
Non compulsory contribution rate.
•
•
Contribution risk assumed by pension plan
•
Risks assumed by individual
Life / Pension Insurance
£
Lifestyle Insurance
£
£
£
RBS00000
Above charts are for illustrative purposes only.
Source: RBS, October 2014
5
Lifestyle Insurance - Demand for growth and protection
Why would an individual want the financial markets to decide when and how you retire?
•
DC schemes at present do little to address all the “real-world” risks of retirement:
– Longevity/Spending risk – an individual outliving their available pension savings
– Purchasing power risk – the “real” pensionable amount being eroded by inflation/wage growth
– Timing risk – short term market moves affecting the pension pot size close to retirement
– Investment risk – getting the incorrect investment strategy leading to a reduction in pension pot.
•
Individuals risk aversion increases with age, suggesting some form of “life-styling” should be implemented . More importantly, evidence
suggests that their loss aversion increases with age.
Preference
50% of assets
Protection
Asset Management
Equities
Insurance
Multi-asset
Source: RBS, October 2014
6
Age
RBS00000
Above charts are for illustrative purposes only.
Fixed Income
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Demand for growth and protection – theory and evidence
The desire to manage Loss Aversion leads to a protected growth strategy being optimal in certain
circumstances. A large number of US annuity purchasers of annuities appear to agree.
Annuity Sales
Prospect Theory
Assume Loss Aversion  Target-driven “threshold” strategy.
64% of the US Annuity market has protected growth characteristics
($230bln market in 2013)
“Threshold” Strategy  Portfolio Insurance Strategy
Utlity Function
Fixed Annuity,
18%
Target
Purchasing
Power
VA (Funds only),
18%
Level
EIA (Annuity with
index lined
crediting), 18%
VA (with
Guaranteed
Living Benefits),
47%
Source: “Target-driven investing: Optimal investment strategies in
defined contribution pension plans under loss aversion” David Blake,
Douglas Wright and Yumeng Zhang.
Source : LIMRA
References Kahneman and Tversky (1979)
Source: RBS, October 2014
7
RBS00000
Above charts are for illustrative purposes only.
Policyholder recap
Marketplace for lifestyle protected products is consistent with policyholder loss aversion :
•
Demand for lifestyle insurance through capital protected product is consistent with policyholder loss aversion.
•
Policyholder loss aversion has underlying economic drivers
– Actual income level versus target income level. Target income level will vary with alternative provision (e.g. DB or state
pensions).
– Relative downside to upside aversion.
•
Focus of policyholder should be less on the investment returns rather and more on the lifestyle outcome
– Policyholder investment insurance
– Criticism of investment returns often takes no account of the protection received
•
Policyholders target is generally not geography specific. However the way those demands are met is very dependant on
geography.
– Government and corporate provision
– Tax deferral incentives
RBS00000
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Product Carriers/Providers
Managing the policyholder demand for Portfolio Insurance
Asset, risk and capital management
Volatility control - volatility of volatility mitigation
10
Portfolio Insurance Strategies
Capital protected Portfolio Insurance (PI) solutions can be devised to provide products with floors and
market upside. They effectively convert convex obligations to policyholders into fixed future cash flows.
Replication method
Capital
Key Carrier Risks
Policy Costs
X (no longer acceptable)
Gap
+ Undefined economic Risk
Premium
Constant Proportion – CPPI
(Perold 1986)
Risk based
Gap
Slippage + Gap Protection
Option Based - OBPI
– Realised/Replicated
Merton (1986)
Risk based
Gap
+ Realised Volatility slippage
Premium + Gap Protection
Counterparty
(premium)
Variable Cost
Implied
Volatility
Realised volatility + gap
+ implied slippage
Risk based
Gap + Managed Costs
Realised
Volatility
 Target Volatility +
Counterparty
(premium)
Fixed Cost
Implied
Volatility
 Target Volatility +
Underwrite
Option Based - OBPI
– Implied/Out-sourced replictation
Black-Scholes (1987)
Targeted Risk Return - TRRPI
– Realised Volatility Controlled OBPI
(various late 1990’s/early 2000’s)
Volatility Control Options
– Implied Volatility Controlled OBPI
Fixed Traditional Annuity
Risk based
Duration
gap protection
implied slippage
Liquidity / Yield
Source: RBS
RBS00000
10
5
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Risk management: Delivering growth with protection
Portfolio Insurance
(CPPI, OBPI)
CPPI
Call options
Active management
(including gap)
Options on
dynamic assets
(volatility control,
dynamic indices)
Put options
Dynamic asset allocation
Lifestyle / target date
Hard protection:
Underwritten downside
protection
Soft protection:
Risk managed hard
protection:
Manager attempts to limit
downside through asset
management
Risk adjusted asset
management with
underwritten protection
RBS00000
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Carrier risks when writing asset value protection
Products carriers faces significant operating and capital risks when underwriting guarantees
Asset Volatility
Interest rates
Value of future protected cash flow
~ PV( Probability of the cash flow )
Dividends
Correlation
Product design
Switch to total return payoff
Basis
Biometric
Limit choices
(simplify)
Re-insure
Pooling
2nd Order
Lapse
Product design
RBS00000
12
6
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Mitigating Volatility of Volatility – typical approach of FIA carriers
1. Volatility Controlled calls price variation v SPX call options
1. Volatility controlled options have significantly less price
instability – price driven mainly by interest rates
SPX (2Y ATM Call)
2. Point-to-point call spread prices are stable. Monthly
arithmetic cliquets cap large asset returns.
15%
10%
- directly through spread of long/short dated volatility
5%
- continually through purchasing volatility options
0%
Oct-04
Oct-06
Oct-08
Oct-10
Oct-12
10Y - 1Y realised (LHS)
SPX (2Y ATM 100/110 Call Spread)
1M realised (RHS)
SPX 10Y ATM-1Y realised Vol. Spread
20%
Option Cost
25%
20%
SPX 3M realised
0%
15%
10%
10%
20%
5%
15%
0%
Oct-04
-5%
10%
30%
Oct-06
Oct-08
Oct-10
-10%
5%
Oct-06
Oct-08
Oct-10
Oct-12
Oct-14
3. Charge additional hedge costs back to policyholder
2. Call spread and lower volatility control ER options v SPX call options
0%
Oct-04
9% VT ER 2Y ATM Option
20%
3. Charge the end policyholder the excess hedging costs
SPX (2Y ATM Call)
5% VT ER 2Y ATM Option
8% VT TR 2Y ATM Option
Option Cost
25%
Oct-14
Oct-12
40%
Oct-14
50%
-15%
60%
-20%
70%
-25%
80%
13
RBS00000
Source all charts: RBS, October 2014
Carrier Recap
Fixed Index Annuity carriers have responded to volatility risk through various methods
•
Volatility Controlled Underlying
– Direct mitigation of volatility of volatility of the asset base
– Uncapped upside
– Multi-asset Dynamic Strategy
•
Capped point-to-point or cliquet offering
– Access to liquid options market – SPX, NDX
•
Volatility/VIX based fee structures
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Regulation and Distribution
Solvency capital based on wholesale markets
Consumer protection
Innovation
Regulation
Regulation of insurance capital has involved from trust through to full open market valuation.
Trust – based on the trustworthiness (and pockets!) of the underwriter.
“I promise to … “
Ability – encouraged good asset management
Be able to pay short term cash flow
Effective asset management for longer dated liabilities
Can ignore market moves to some degree
Dependent on mean reversion and risk premium assumptions
Solvency – encourages both good asst and risk management.
Be short term market value solvent, based on market based Value at Risk measures
Have sufficient contingency to transfer business in a short horizon (e.g. 1-year)
Greater capital requirements
Effective risk management
Can not ignore market moves
RBS00000
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Solvency II
Higher capital requirements are leading insurers to target less capital-intensive products
Surplus
Surplus
Surplus
Free Assets
Free Assets
(Traffic Light)
Solvency I Capital
Requirement
Reserves
Assets
(Book Value)
Free Assets
Solvency I.5
Capital
Requirement
(Discounted at
guarantee)
Assets
Liabilities
Solvency II Capital
Requirement
Risk Margin
Assets
(Market Value)
Reserves:
Best Estimate
Liabilities
Assets
(Market Value)
Reserves:
Best Estimate
Liabilities
Assets
Liabilities
Assets
Liabilities
Low rates, low
guarantees & high
capital requirements,
have led insurance
companies towards a
unit linked dominated
offering
Increased capital requirements for insurers exposures
RBS00000
17
Distribution
Consumer protection, advice, regulation, innovation, fees
•
Advice: Whole-life, whole-of-market review
– Increasing consumer protection has also driven products to be simpler and more transparent
– Is the product correct for your circumstances? Income, Savings, Risk.
– In UK, market is influence by need to advice on an individual’s complete financial position and what is available
•
Regulation
– Mis-selling
– Are the terms and conditions of the policy fair?
– Is the pricing fair and can it rise in the future?
– Conflicts of interest
•
Innovation
– Something new (new idea to sell), something old (but understandable by the policholder)
– Consumer Education. (Example, volatility control is based upon a “risk budget” not a “notional budget”)
– Asset and risk management combined is attractive proposition.
RBS00000
18
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Merging the requirements
Product innovation has been driven by risk benefit transfer
Volatility control – shares the burden?
Products: Driven by shifting policyholder/carrier risk tolerance
Product development has alternated the “risk management benefit” between carrier and policyholder
Policyholder
Carrier
Risk Benefit
Traditional
Risk management
1976-1986
With-Profits
theoretical framework
Product
Portfolio
Insurance
1987 Market Crash
Crash risk/liquidity
undervalued
Proetcted
Return
Incorrect rate
Falling Rates
Protected
Product
option hedge
Return
Rate Adjusted
Product
Make VA more attractive
No protection
Vanilla
Variable
Living benefits
Annuity
Offered
Market Volatility
Living
Undervalued
Benefits
Rates lows, Volatility high,
?
lifestyle protection
RBS00000
20
10
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Volatility Control – balancing the requirement of stakeholders
Why we believe Volatility control is the leading risk management technique for product innovation
•
For the policyholder it offers innovative payout profiles with improved product economics, benefits may include:
– Uncapped returns on multi-asset strategies
– Embedded risk and asset management (include life-styling, risk-parity etc.)
– Transparent option pricing
– Short or long tenors
•
For the carrier
– Mitigates volatility of volatility
– Stability of pricing
– Ability to plan risk management programs
– Helps product design features
•
For the distributor
– Innovative underlying
– Attractive product choices
•
For the regulator
– Market based risk management
RBS00000
21
Please score the crediting mechanism.
Crediting Type
Volatility Control Index
Upside
Call Spreads
Volatility linked fees
Carrier
Financial Markets
Distributor
Regulator
Policyholder
Cumulative Score
RBS00000
22
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Pros & Cons of Volatility Control
Strengths
Limitations
•
•
No discretionary overlay to change the investment
strategy (strictly rules-based)
•
Potential underperformance versus a benchmark
index in the presence of a positive spot volatility
correlation (Potentially less returns in sharp
rebounds)
•
Limited end investor knowledge and “risk” based
investing
•
Volatility Control is designed to be more hedging
friendly from the outset by potentially:
–
stabilising the cost of hedging over defined
periods
–
reducing the cost of hedging
–
allowing hedging of longer tenors
Can be customised to suit preferences with respect
to:
–
risk/ return profiles (Volatility Target)
–
diversification & cross asset selection
–
static or dynamic asset allocation
•
Relevant concept given current market focus on risk
management & cost efficiency
•
Greater product development flexibilities that may
better meet the needs of investors
–
May require investor education
–
May require sales and infrastructure updates
23
RBS00000
The description may not include a comprehensive list of the associated risks
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Impact of Hedging on Capital and Reserves
Date
Alex Marion
VP, Product Management, Numerix
November 17th, 2014
Agenda
•
•
•
•
•
•
•
AG43 Overview
Challenges
Hedging Strategies
Nested Stochastic
Future Greeks
Target Volatility
Real World Models
25
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11/28/2014
Challenges
• Accounting mismatch
–
–
–
–
GAAP versus Statutory
Dynamic hedging strategies focus on economic risk
Statutory calculations focus on surplus or deficiency
A “good” GAAP hedging strategy may increase statutory reserves
• Modeling Challenges
– Simulating hedging requires integrated asset and liability model
• Need model that captures all economic risks such as equity, rates, credit,
and stochastic vol
–
–
–
–
Nested stochastic simulation
Scenario calibration requirements
Computational challenge
Managed volatility funds
25
Hedging Strategies
• Dynamic Hedging Strategies
– Goal is to manage GAAP financials and economic risk
– Manage balance sheet locations due to mark to market of liability
guarantees
• Delta hedging
– Immunize economic risk of being short an option
– GAAP and Statutory sensitivity
• Rho hedging
– More focus for GAAP
• Vega hedging
– May not represent a true economic hedge
– Expensive due to significant risk premia in volatility hedges
– Little to no sensitivity for statutory reserves
25
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Holistic Risk Management
• What is the right tradeoff between managing GAAP versus
Statutory?
• Understanding how hedging impacts balance sheet
• Vega hedging may negatively impact CTE calculation
• However, choice of hedges may reduce SS amount
• Managed vol product features may reduce the burden for
managing GAAP risk
• Ability to project hedging strategies is highly valuable, and a
critical for assessing risk across multiple reporting regimes
25
Nested Stochastic – Overview
• The nested stochastic pattern:
– Outer-Loop: real-world dynamics
– Inner-Loop: risk-neutral dynamics
6
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AAA Scenario Generation
• Equity market returns
– Monthly SLV model for U.S. equity, international diversified equity, intermediate risk equity, and
aggressive/exotic equity
7
Liability Projections
Sample GLWB
• Withdrawal rate based on age at withdrawal: 4%/5%/6% vs ages <65/75>
• Annual ratchet; 5% simple annual rollup during deferral period up to 10Y
• Rider fee is 0.70%; M&E is 1.20%; fund fees are assumed zero
• AAA Real World Scenarios
25
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Liability Greeks
25
Target Volatility
If an asset has predictable volatility, then you can construct a selffinancing portfolio with locally constant volatility
• The portfolio consists of a risky-asset like the S&P 500 and a
low-risk asset, here it is a constant maturity zero coupon bond
• The allocation changes dynamically based on future expected
volatility and the volatility target
• The strategy includes some rebalancing frictions
5
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Target Volatility
10% Target Volatility strategy reduces future potential vega
25
Target Volatility + Capital Protection
Adding capital protection further reduces claim vega
25
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Fee Indexing Dynamics
Fee-indexing increases fees when volatility rises, typically linked
to the VIX Index
VIX Indexed Fees
VIX indexed fees create positive vega in the fee leg, reducing
overall total vega
25
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Stochastic Volatility and the Volatility Premium
• NAIC allows use of alternative models that satisfy the
calibration criteria
• A real world extension of a stochastic volatility model provides
a good candidate
• Take for instance the real-world Heston (1993) model,
݀ܵ
= (‫ݎ‬+ ߶ܸ)݀‫ݐ‬+ ܸܹ݀ ଵ
ܵ
ܸ݀ = ߢ ܸஶ − ܸ ݀‫ݐ‬+ ߦ ܸܹ݀ ଶ
with < ܹ݀ ଵ, ܹ݀ ଶ > = ߩ.
• Here, ߶ > 0 governs equity premia scaled with vol
• Higher reversion and lower long-run volatility under real-world
dynamics.
25
Calibrating Heston to NAIC Criteria
• We can find a set of parameters that produces a model satisfying
all the NAIC calibration criteria
ܸஶ = 0.029, ߦ = 0.1, ߶ = 2.0, ߢ = 2.5 , ߩ = − 0.7
RW Heston Projection Percentiles
Percentile
1 Year
5 Year
2.50%
0.77
0.68
5.00%
0.81
0.76
10.00%
0.87
0.88
90.00%
1.32
2.28
95.00%
1.39
2.58
97.50%
1.44
2.95
10 Year
0.74
0.87
1.10
4.02
4.79
5.51
20 Year
0.99
1.34
1.65
10.89
14.54
17.77
Numerix RW Heston minus NAIC
Percentile
1 Year
5 Year
10 Year 20 Year
2.50%
(0.01)
(0.04)
(0.05)
5.00%
(0.03)
(0.05)
(0.07)
(0.17)
10.00%
(0.03)
(0.06)
(0.06)
(0.45)
90.00%
0.04
0.11
0.39
1.87
95.00%
0.04
0.13
0.43
2.84
97.50%
0.02
0.23
0.39
* Based on 1000 scenarios over 30 years, monthly.
42
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11/28/2014
Calibrating Heston to NAIC Criteria
• Heston retains fit with fewer paths
500 Scenarios
RW Heston Projection Percentiles
Percentile
1 Year
2.50%
0.77
5.00%
0.80
10.00%
0.85
90.00%
1.33
95.00%
1.41
97.50%
1.47
100 Scenarios
5 Year
0.67
0.76
0.90
2.27
2.61
2.96
10 Year
0.71
0.89
1.08
4.04
4.83
5.81
20 Year
1.04
1.36
1.63
10.70
14.05
16.93
Numerix RW Heston minus NAIC
Percentile
1 Year
5 Year 10 Year 20 Year
2.50%
(0.01)
(0.05)
(0.08)
5.00%
(0.04)
(0.05)
(0.05)
(0.15)
10.00%
(0.05)
(0.04)
(0.08)
(0.47)
90.00%
0.05
0.10
0.41
1.68
95.00%
0.06
0.16
0.47
2.35
97.50%
0.05
0.24
0.69
RW Heston Projection Percentiles
Percentile
1 Year
2.50%
0.78
5.00%
0.81
10.00%
0.83
90.00%
1.38
95.00%
1.46
97.50%
1.54
5 Year
0.72
0.75
1.01
2.21
2.49
2.93
10 Year
0.88
0.99
1.08
4.17
4.64
5.55
20 Year
1.06
1.23
1.63
10.72
11.97
13.90
Numerix RW Heston minus NAIC
Percentile
1 Year
5 Year 10 Year 20 Year
2.50%
(0.00)
(0.00)
0.09
5.00%
(0.03)
(0.06)
0.05
(0.28)
10.00%
(0.07)
0.07
(0.08)
(0.47)
90.00%
0.10
0.04
0.54
1.70
95.00%
0.11
0.04
0.28
0.27
97.50%
0.12
0.21
0.43
42
The Nested Stochastic Pattern - Heston
• Generate S and V according to real world Heston
݀ܵ
= (‫ݎ‬+ ߶ܸ)݀‫ݐ‬+ ܸܹ݀ ଵ
ܵ
ܸ݀ = ߢ ܸஶ − ܸ ݀‫ݐ‬+ ߦ ܸܹ݀ ଶ
with < ܹ݀ ଵ, ܹ݀ ଶ > = ߩ.
• At each node, evolve S and V in a nested model according to
risk-neutral Heston
݀ܵ
= ‫ݐ݀ݎ‬+ ܸܹ݀෩ଵ
ܵ
ܸ݀ = ߢ̃ ܸ෨ஶ − ܸ ݀‫ݐ‬+ ߦ ܸܹ݀෩ଶ
with < ܹ݀෩ଵ, ܹ݀෩ଶ > = ߩ
25
9
11/28/2014
Real World Heston
• Advantages of using real world Heston
– Model consistency between outer loop and inner loops
– Consistent dynamics may improve simulated hedging results
– Analytic pricing for vanillas
Hedge P&L Bleed with Black Model
Heston is a Good Hedging Model
Liability
Liability
Replicating Portfolio (Heston)
Replicating Portfolio (Black)
*this study simulates dynamic hedging for FIA
25
Thank You!
Contact :
Alex Marion,
VP, Product Management
[email protected]
Follow :
Twitter:
@nxanalytics
@Marion2025
LinkedIn:
http://linkd.in/Numerix http://linkd.in/AlexMarionNx
10
11/28/2014
FAS157, AG36, and Aligning
Accounting Methodology with
Hedging Strategy
10th Annual EBIG Conference (Chicago)
Session 2B
17 November 2014 (1330 -1500 hours)
Brian Boucher, ASA
Transamerica Life & Protection
ALM/Hedging Group
INTERNAL
What is the Goal of Hedging?
• The Quant answer
– “To reduce the economic profit or loss impacts
from current-period market movements to zero.”
• The C-Suite answer
– “To reduce the below-the-line earnings impacts
from current-period market movements to zero.”
• This presentation explores the difference in
these two perspectives as it applies to IUL
products
1
11/28/2014
Case Study: Product Summary
• Universal Life Plan Features
– Premiums net of insurance/expense charges accumulate AV
– Level death benefit, cost-of-insurance charges proportional to
NAR = Face - AV
– Cash value at surrender is AV less charges during penalty period
• Indexed Universal Life Plan Features
– Index Account with annual point-to-point crediting based on XYZ
index
– Currently 0% floor, 12% cap, 100% participation rate
– Cap is “floating” with 1% guaranteed minimum
– Fixed Account crediting 3% (with 2% guaranteed) also available
– Policyholder can reallocate funds at anniversary dates
The General Principle of Hedge Accounting
170
160
150
Interest Earned in the current period reflects
mark-to-market adjustments in that period alone.
Interest Credited in the current period reflects
index performance over a longer historical period.
140
130
120
110
100
90
Solution: Mark down liabilities on the
balance sheet to offset negative spread.
Current
Reporting
Period
80
2
11/28/2014
Case Study: Option Values
• Assume 0.5% risk free rate and 2% dividend rate
on XYZ index at issue date.
– Floor = ATM 1-year call option @ 20% implied vol
• Black-Scholes Cost of Floor = 7.15%
– Cap = 112% OTM 1-year call option @ 17% implied vol
• Black-Scholes Cost of Cap = 2.31%
– Net Option Cost = 4.84%
• If the index rises 5%, the net option value
increases to 6.11%
• If volatility increases 5% across the board, the
net option value increases to 5.13%
Short-Term or Long-Term Liabilities?
95% Survival
Issue
Date
Valuation Crediting
Date
Date
Policies surrendering in this
period receive $0 payout
Cash Value
Becomes Positive
75% Survival
• A policyholder pays $100 in premium…
– Should the company hedge on $95 or $75?
– If the policyholder lapses before CV > 0, they never
actually receive the index credit, so economically the
answer is $75 – less risk for the same payout.
– But can the company’s accounting reflect that?
• Consider… will $95 of crediting be realized as an expense
before $20 is “collected” as surrender charge?
3
11/28/2014
AG36 and FAS157
FAS157
Embedded
Derivative
Value of
Future
Crediting
Standard
CRVM
Reserve
Actuarial
Guideline
XXXVI
Reserve
US Statutory Valuation
FAS97
Benefit
Reserve
(Account
Value)
Value of Future
Crediting
Value of Past
Crediting
FAS157
Host
IFRS/US GAAP Valuation
Actuarial Guideline XXXVI
• Background: Universal Life policies usually subject
to CRVM under Standard Valuation Law
– Conservative benefit reserve calculation using
proscribed mortality assumptions (2001 CSO, etc)
– Projects benefits under “guaranteed” assumptions,
runs off against a stream of NLPs calculated at issue
• AG36 provides 3 methods for valuing Indexed
Universal Life deemed “consistent with CRVM”
– Implied Guarantee Rate Method (IGRM) – Type 1
– Updated Average Market Value (UAMV) – Type 2a
– Updated Market Value (UMV) – Type 2
4
11/28/2014
Actuarial Guideline XXXVI Methods
• IGRM (Type 1) is a book value method
– Must meet strict “hedged as required” criteria
– As with CRVM, valuation rates determined at issue
• UAMV and UMV (Type 2) are market value methods
– No hedging requirement, although a company may still
hedge
– UAMV requires term  1 year, and may exclude higher
caps/participation rates
– UMV has no requirements
– UAMV and UMV differ in treatment of index guarantees
beyond the current year
Actuarial Guideline XXXVI
“Hedged as Required” Criteria
• Allows for either
– Static hedging with options (the “Basic” criteria)
– Dynamic hedging with stock or futures (the “Option Replication”
criteria)
• Both require:
– Decrementing notional amounts by < 6% per year
– Specific plans for unexpected cash flows (surrenders etc)
– Monitoring process for efficiency with stated tolerances
• The Option Replication criteria also requires the hedge to
be at least 90% effective in matching asset and liability
movements
• These criteria may be difficult for most companies to
meet, or too restrictive if needs change.
5
11/28/2014
Actuarial Guideline XXXVI Illustration
Additional death benefits
add to CRVM reserve, if
the life survives to this point
Value of
Death Benefit
Face
Value
Funds allocated to the
index account credit at
4.9% during this year only
Issue
Date
Valuation
Date
Guaranteed
Maturity
Fund Value
(Accrues at 2%)
*Note that GMF accumulation assumes maximum
charges, minimum crediting, and that lapse and
surrender are ignored (mortality decrement only)
Maturity
Date
Actuarial Guideline XXXVI Final Notes
• Statutory concerned with orderly liquidation
– Emphasis on guarantees – what you can do, not
what you will do
– Company can lower cap to 1%, policyholder can
guarantee herself at least 2% in the fixed account
• Simple exercise: What does $1 added to
today’s fund value add to the CRVM reserve?
– Apply this ratio to a standard option valuation, if
index guarantees are short term
6
11/28/2014
Statement of Financial Accounting Standards
No. 157 – Fair Value Measurements
• 2007 US GAAP standard – parallels IFRS 13
• Clarifies “fair value” definition for instruments
previously valued under FAS133 (such as IUL)
• Differences with AG36
–
–
–
–
Best estimate assumptions for lapse/surrender/mortality
Best estimate for index guarantees beyond current term
Assumes no future premiums paid (no GMP/NLP)
Bifurcated reserve vs single “modified” CRVM
• Similarities with AG36
– Both fair value, unless using IGRM
– Both translate short-term crediting to long-term CFs
FAS157 Basics
• Views an IUL contract as a “hybrid” of
– Host insurance contract (cash flows such as death
benefits that occur without any index crediting)
– Embedded Derivative (ED) contract sensitive to index
movements
• The ED is valued using fair value, including a
possible adjustment for nonperformance risk
• The Host contract is valued by imputing an
interest rate to each premium contribution
• Both values reflect only benefits paid for by
premiums to date
7
11/28/2014
FAS157 Case Study
• Policyholder pays single $100 premium
– This is expected to keep the policy in force 1 year
– At the end of the year, index returns 20%
• The ED at issue is $4.84, the ED at the end of the
year is $12.00 (i.e. at the 12% cap)
– This $7.16 must be earned by the hedge assets
• The Host at issue ($95.16) must support the
return of $100 premium at the end of the year
– This is an implied IR of 100/95.16 – 1 = 5.09%
– This sets a minimum earned rate on General Account
assets
FAS157 Income Statement
• Fixed Income Interest Margin
– Interest Earned on General Account Assets, minus
– Interest Credited on FAS157 Host Contract
– Measures whether pricing is “supportable”
• Equity Margin
– Mark-to-Market P/L on Hedge Assets, minus
– Increase in FAS157 Embedded Derivative
– Measures performance of hedging program
8
11/28/2014
FAS157 Income Statement
• No “excess” interest credited (i.e. crediting
due to index) appears on income statement
Projected
Index Credit
Issue
Date
Valuation
Date
Actual
Index Credit
Issue
Date
Projected Benefit Cash Flows
Paid for by Projected Index Credit
Valuation
Date
Projected Benefit Cash Flows
Paid for by Actual Index Credit
No change in ED due to simply
passing the crediting date,
although the equity risk is gone
Benchmarking FAS157 Results
• Change in AV (FAS97 Reserve)
= Total Premiums – Deductions – Fund Release
+ Guaranteed Crediting + Index Crediting
• Change in FAS157 Host
= Host Premiums – Deductions – Host Release
+ Crediting at Host Imputed IR
• Change in FAS157 Embedded Derivative
≈ Change in Future Crediting
+ Index Crediting – ED Release
• The “historic” crediting component of ED is not equity
sensitive and runs off over time
9
11/28/2014
FAS97 and FAS157
• Under FAS97, interest margin is a component of
both gross profit and revenue
– It therefore affects amortization of all intangibles
• Interest margins are smaller and less volatile if a
hedging program is in place and FAS157
accounting is used
– Is it reasonable to assume expected interest margins
of zero for the purposes of FAS97?
– Hedge gains and losses are realized as variances in the
current period
Final Considerations
• Requirements of “Fair Value”
– Liabilities discounted using own credit spread for
nonperformance risk
– Calibrated to observable market securities where
possible (exit prices, i.e. after bid/ask spread)
– For floor/cap plans, need to include volatility skew
• FAS157 accounting potentially allows for an
alignment of the economic view and the earnings
reality
– Returning to the example from earlier, might be able
to hedge on $75 notional without realizing the $95
credit as an expense in that period
10
11/28/2014
Final Considerations
• Attribution is key
– First, separate
• pricing decisions (setting the cap or participation rate)
• hedging decisions (buying or selling securities)
• FAS157 provides a natural framework for this in the
bifurcated reserve
– For “Option Replication” hedges, quantify and
monitor unhedged risks (gamma, vega, etc)
• The AG36 “Hedged as Required” criteria can serve as a
guideline here even if the company does not use IGRM
11
Influence of Computing and Models on Risk
Management
A Discussion on Least-Squares Monte Carlo Simulation
Peter M. Philips
Session 3A: November 17, 2014 3:30 – 5:00 pm
Prepared by Aon Benfield Securities Inc.
Annuity Solutions Group
Legal disclaimer
Aon Benfield Securities, Inc. and its affiliates (“Aon”) are not currently registered in any securities
advisory capacity in any Canadian jurisdictions. These materials are not being provided under any
advisory mandate and shall not be viewed or construed as any type of investment advice, including but
not limited to investment advice tailored to you or any other party. Therefore, these materials shall not be
relied upon as investment advice in any circumstances. These materials contain information that is
confidential or proprietary to Aon and shall not be disclosed to any third party without the express written
consent of Aon.
This document is the confidential property of Aon Benfield Securities, Inc. (“Aon”), has been prepared by
Aon for informational purposes only and is intended only for the designated recipient. As a condition to
reviewing this document, the recipient agrees that without the prior written consent of Aon, which may be
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or services are offered solely through Aon Benfield Securities, Inc. or an appropriately licensed affiliate.
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
2
1
Overview
 Discuss the use of least-squares Monte Carlo (LSMC) techniques for nested
stochastic simulations
– LSMC is a type of Proxy Modeling technique which includes such approaches as
Curve fitting, Compression, etc
– The Insurance Industry is facing increasing computational demands as result of
increased product and scenario generation complexity, financial reporting
requirements, capital standards and enterprise risk management activities
 What we are presenting today is different from previous work because of the
following:
– The application of LSMC to Greeks and VA hedging simulations
– Multiple time horizons and stochastic rates
– Comparison of LSMC results to brute-force simulated nested stochastic simulated
Book level results
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
3
PathWise Simulation Workflow
 PathWise is an integrated solution from creating the model, launching the job and
collecting the outputs
PathWiseTM
Analytics Studio
P
PathWiseTM
Modeling Studio
GPU Grid
PathWise
Middleware
AWS Cloud
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
4
2
Contents
Section 1
Brute-Force Monte Carlo (BFMC)
Section 2
Least-Squares Monte Carlo (LSMC)
Section 3
Conclusions
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
5
Section 1: Brute-Force Monte Carlo (BFMC)
3
Brute-Force Monte Carlo Simulation
 The Brute-Force Monte Carlo (BFMC) nested stochastic simulation is very intensive
computationally. Legacy VA solution providers struggle with these problems.
 A typical BFMC nested stochastic simulation, for example, has 2000 outer loop paths
and 41 quarterly steps.
Risk Neutral Paths (1000)
Real World
Paths (2000)
0
1
2
….
3
Time Steps (41)
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
7
One Sample Path
 50+ state variables are projected including account value, guarantee values, fees and
withdrawals
Discount
Step Factor
Index 1
Index 5
Index 1 Return …. Index 5 Return
Portfolio
Value
Portfolio
Return
Shadow
Account
Account
Withdrawal
Bond 1
Bond 2
Bond 3
.…
0
1.00
100.00
0.00
100.00
0.00
100.00
0.00
100.00
0.00
0.00
100.00
100.00
100.00
1
1.00
98.19
-0.02
100.56
0.01
100.56
0.01
98.21
-0.02
0.83
99.72
99.72
99.72
2
0.99
98.37
0.00
101.94
0.01
101.94
0.01
96.92
-0.01
0.83
99.44
99.44
99.44
3
0.99
88.51
-0.11
112.10
0.10
112.10
0.10
91.46
-0.06
0.83
99.16
99.16
99.16
4
0.99
75.94
-0.15
110.59
-0.01
110.59
-0.01
84.97
-0.07
0.83
98.91
98.91
98.91
107.09
-0.03
107.09
-0.03
85.31
0.00
0.83
98.67
98.67
98.67 ….
5
0.99
75.00
-0.01 ….
6
0.98
69.28
-0.08
106.14
-0.01
106.14
-0.01
78.38
-0.08
0.83
98.42
98.42
98.42
7
0.98
66.05
-0.05
105.84
0.00
105.84
0.00
75.15
-0.04
0.83
98.16
98.16
98.16
8
0.98
59.06
-0.11
96.67
-0.09
96.67
-0.09
71.39
-0.05
0.83
97.93
97.93
97.93
9
0.98
53.81
-0.09
95.02
-0.02
95.02
-0.02
70.50
-0.01
0.83
97.93
97.62
97.62
10
0.97
61.85
0.14
104.99
0.10
104.99
0.10
75.55
0.07
0.83
97.93
97.28
97.28
….
….
37
0.83
144.73
0.08
234.80
0.08
128.69
0.08
1.08
76.77
0.83
97.93
93.54
82.84
38
0.82
142.25
-0.02
249.98
0.06
128.28
0.00
0.97
73.77
0.83
97.93
93.54
82.09
39
0.81
129.98
-0.09 ….
282.58
0.12
132.16
0.03
1.00
73.28
0.83
97.93
93.54
81.33 ….
40
0.81
122.11
-0.06
307.87
0.09
136.79
0.03
1.01
73.37
0.83
97.93
93.54
80.59
41
0.80
126.18
0.03
336.11
0.09
145.59
0.06
1.05
76.57
0.83
97.93
93.54
79.85
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
8
4
AWS' Global Infrastructure Translates Into Capacity for Us
GovCloud
US ITAR
Region
US West
N.
California
Oregon
US East
South
America
EU
N. Virginia
Sao Paulo
Ireland
Asia Pacific
Sydney
Tokyo
Singapore
China
China
10 Regions | 25 Availability Zones | 51 Edge Locations
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
9
AWS also Enables Cost Effective BFMC Simulations
No Upfront
Investment
Replace capital expenditure with
variable expense
Low ongoing cost
Customers leverage our
economies of scale
Easily Scale Up &
Down
No need to guess capacity
requirements and over-provision
Has everything
you need at
transparent
cost
Speed and agility
Focus on business
Global Reach
Infrastructure in minutes not
weeks
Excellent Support Model
Go global in minutes and reach
a global audience
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
10
5
Amazon GPU VPC Scalability Results
 AWS g2 GPU* virtual private cloud proves linearly scalable for large scale VA simulations
when using PathWise.
AWS Scalability
Total Paths (Million)
60
40
Total Paths/Second (Million)
20
0
0
50
100
150
200
250
300
# of Cores (Thousand)
# of GPUs
5
10
50
100
150
200
# of Cores (Thousand)
7.68
15.36
76.8
153.6
230.4
307.2
Total Paths/Second (Million)
1.50
2.99
14.89
29.94
44.64
59.75
Throughputs/Second (Thousand)
299
299
298
299
298
299
*each GPU or g2.4xlarge instance in AWS has 1536 cores and 4 GB of
video memory
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
11
Inner Loop Scenarios
Risk-Neutral Paths at Each Pivot Point:
Shocks (17)
Least Squares Scenarios (20)
Brute Force Scenarios (1000)
For one policy:
Brute Force
Least
Squares
Fitted
Time
Steps
41
41
41
Outer Loop
2000
2000
2000
Inner Loop
1000
20
0*
Shocks
17
17
17
Total Paths
1,394,000,000
27,880,000
82,000
* Fitted FMV and Greeks
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
12
6
Dynamic Hedge Model Simulation Settings
 Liability:
– 10,000 policies
– Account value: $2,989,517
– 5 underlying assets / weights: 40%, 10%, 33%, 7%, 10%
– 30 year GMWB
– Withdrawal Base: 100%
– Withdrawal Frequency: quarterly
 Economic Scenarios:
– Outer loop: Regime-Switch Lognormal with HW2 stochastic rates
– Inner loop: Geometric Brownian Motion with HW2 stochastic rates
 Greeks Computed:
– 5 Deltas
– 3 Rhos
– Two-sided, produced by a total of 17 shocks
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
13
No. of Simulated Paths and GPU Performance
 BFMC Simulation:
– Inforce: 10,000 policies
– Outer loop: 2000 paths
– Outer steps: 40 quarters
– Inner loop: 1000 paths
– Shocks: 17
– Amazon GPUs:150
– Run Time:~300,000 seconds
# policies
10000
# outer loop
x
2000
 GPU Throughput =
# outer steps
x
40
x
1000
்௢௧௔௟௦௜௠ ௨௟௔௧௘ௗ ௣௔௧௛௦
ீ௉௎ ௡௨௠ ௕௘௥∗ோ௨௡ ௧௜௠ ௘(௦௘௖௢௡ௗ)
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
# shocks
# inner loop
x
17 =
≈ 300,000
13 Trillion Simulated Paths
paths/GPU/second
14
7
BFMC Hedging Simulation Results
 Hedging works as expected
 Much smaller standard deviation
 Tail risk is dramatically reduced
Naked (BFMC)
Hedged (BFMC)
Mean
-1,676
-417
STDEV
213,836
30,572
7x
CTE95
-577,532
-73,759
7.8x
CTE99
-737,335
-104,389
7x
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
Improvement
15
Section 2: Least-Squares Monte Carlo (LSMC)
8
LSMC Motivation and Concepts
 The previous experiment could take weeks for most CPU-based legacy solutions to
calculate--even with thousands of cores.
 LSMC method offers an analytical solution to model the liabilities and Greeks in terms of
risks that drive the business
 Monte Carlo Step: numerically approximate the liability value by randomly sampling
outer loop paths where a small number of scenarios (fitting points) are run to reduce run
time. An expected liability value is calculated for each fitting point.
 Least-Squares: run a regression through these fitting points and their corresponding
liability values to obtain a proxy function that approximates the true liabilities
 Use the proxy function to determine the liability value for each real world scenario
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
17
LSMC Method
 Arrive at a proxy function by performing regression on fitting points and their
corresponding liability values
Proxy Function
An analytical formula for the liability as a function of risk
drivers, e.g.
ܻ = ܽ଴ + ܽଵܴ‫ܦ‬ଵ + ܽଶܴ‫ܦ‬ଵଶ + ܽଷܴ‫ܦ‬ଶ + ܽସܴ‫ܦ‬ଶଶ
+ ⋯ + ܽହܴ‫ܦ‬ଵܴ‫ܦ‬ଶ + ܽ଺ܴ‫ܦ‬ଵଶܴ‫ܦ‬ଶ + ⋯
Fitted Proxy Function
 Calibration like problem
 Different formulations for FMV and Greeks
 A lot of time and effort may be required to find the
appropriate specification
Time 0
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
Time 1
18
9
LSMC – Experiment 1: European Put Option FMV at T=1
Study 5-year expired Put Option
Price at the first year:
Least Squares Monte Carlo Simulation
 Spot Value: $100
 Strike Value: $100
 Volatility: 20%
 Interest: 2%
Simulation Setting:
 500 1-year projection points
 5 LSMC inner-loop paths
Results:
Only Three points out of 95% CI
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
19
LSMC – Experiment 1: European Put Option FMV at T=1
Least Squares Monte Carlo Simulation
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
20
10
LSMC – Experiment 1: European Put Option FMV T=1
Least Squares Monte Carlo Simulation
Bad news
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
21
LSMC – Experiment1: European Put Option FMV at T=4
 5-year Put Option Value at
the 4th year
Least Squares Monte Carlo Simulation
 As the maturity approaches,
FMV shape and convexity
change.
 Under this case, the lower
order polynomial regression
cannot give good fitting
results
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
22
11
LSMC – Experiment1: European Put Option at T=4
Least Squares Monte Carlo Simulation
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
23
LSMC – Experiment1: European Put Option at T=4
Least Squares Monte Carlo Simulation
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
24
12
LSMC – Experiment1: European Put Option at T=4
Least Squares Monte Carlo Simulation
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
25
LSMC – Experiment1: European Put Option Delta at T=1
 Put Option Delta at the first
year
Least Squares Monte Carlo Simulation
 The number of LSMC innerloop paths is 50
 From the plot, the deep ITM
and OTM deltas are less
accurate
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
26
13
LSMC – Experiment1: European Put Option Delta at T=1
Least Squares Monte Carlo Simulation
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
27
LSMC – Experiment1: European Put Option Delta at T=1
Least Squares Monte Carlo Simulation
Bad fit at the extremes
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
28
14
LSMC – Experiment1: European Put Option Rho at T=1
 5Y Put Option Rho at the first year
Least Squares Monte Carlo Simulation
 LSMC inner-loops: 50
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
29
LSMC – Experiment1: European Put Option Rho at T=1
Least Squares Monte Carlo Simulation
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
30
15
LSMC – Experiment1: European Put Option Rho at T=1
Least Squares Monte Carlo Simulation
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
31
LSMC – Experiment 2: A Single Policy
 Policy
– Account value: $300
– Five underlying assets / weights: 40%, 10%, 33%, 7%, 10%
– 30 year GMWB
– Withdrawal Base: $300
– Withdrawal Frequency: quarterly
– Fair dividend: 42bps
 Economic Scenarios:
– Outer loop: Regime-Switch Lognormal with HW2 stochastic rates
– Inner loop: Geometric Brownian Motion with HW2 stochastic rates
 Simulation:
– Simulation Frequency: quarterly
– Out loop: 2000 paths
– LSMC Inner loop: 20 paths
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
32
16
LSMC – Experiment 2: A Single Policy
 10 year outer loop
scenarios are projected
 The proportions of the
LSMC fitted values falling in
the 95% confidence
intervals are over 98.5%
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
33
LSMC – Experiment 2: A Single Policy
 The y-axis is the LSMC fitted FMV value. As the red points are around the blue diagonal line,
the fitting results are good.
 The two green lines are the 95% confidence interval band.
 The three R-squared values between BFMC and LSMC fitted FMVs are over 99.9%.
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
34
17
LSMC – Experiment 2: Single Policy Delta
 The proportions of the
LSMC Total Delta values
falling in the 95%
confidence intervals are
between 28% and 60%
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
35
LSMC – Experiment 2: Single Policy Delta

These three R-squared values between BFMC deltas and LSMC fitted deltas are 94.7%, 97.1%, and
97.9% respectively.

Not as good as the fitting for FMV.
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
36
18
LSMC – Experiment 2: Single Policy Rho
 The proportions of the
LSMC Total Rho values
falling in the 95%
confidence intervals are
less than 4%.
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
37
LSMC – Experiment 2: Single Policy Rho
 Liability Rho Fitting Percentage in CI along time
 Three R-Squared values between BFMC Rho and LSMC Rho are 97.6%, 98.2%, and
98.8% respectively.
 Although R-Squared values look good, their slopes are far away from tg45⁰=1. All of
them are close to tg60⁰≈ 1.732.
 The number of LSMC inner loop paths is not enough for LSMC Greeks fitting
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
38
19
LSMC – Experiment 3: Multiple Policies
GMWB Book Information
 10,000 generated 30-year GMWB policies
 Different policies have different total initial account value. The total initial account
amount ranges from $100 to $500
 Different policies have different allocations for sub-accounts
 To calculate FMVs and Greeks during the first decade
Computation Complexity
 Four hundred training sets at each time step(total 40 steps; four steps per year)

For each training set, there are 2000 x 20 projections at each time step (# outer loops x
# LSMC inner loops)
 Totally, there are 400 x 40 x 2000 x 20 projections
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
39
BFMC vs LSMC (Naked)
 Least-squares proxy works very well
for FMV fitting
 Nearly identical naked P&L
distributions
LSMC
BFMC
Mean
-8,643
-1,676
STDEV
209,593
213,836
CTE95
-568,214
-577,532
CTE99
-733,644
-737,334
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
40
20
BFMC vs LSMC (Hedged)
 Least-squares proxy does not work
very well for Greeks
 Least-squares hedging results are not
as good as brute-force
 Larger standard deviation and longer
tail risk
 LSMC produced some biased
hedging statistics
LSMC
BFMC
Mean
-6,139
-417
STDEV
59,941
30,572
CTE95
-155,900
-73,759
CTE99
-225,089
-104,389
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
41
LSMC Hedging Simulation Results
 LSMC hedged performance is not as
good as BFMC
 Less standard deviation deduction,
decreases from BFMC’s 7x to 3.5x
 Less tail risk deduction, decreases
from BFMC’s 7.8x to 3.6x
Naked (LSMC)
Hedged (LSMC)
Mean
-8,643
-6,139
STDEV
209,593
CTE95
-568,214
CTE99
-733,644
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
LSMC
Improvement
BFMC
Improvement
59,941
3.5x
7x
-155,900
3.6x
7.8x
-225,089
3.3x
7x
42
21
Section 3: Findings and Conclusion
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
43
Our LSMC Findings
Pros
Cons
Less inner loop calculations
Fitting required at each time step
and each Greeks
Fast FMV and Greeks calculation
once fitted
Fitting process can be subjective
Confidence intervals can be
calculated
Fitting does not work very well for
Greeks
Negative impact on hedge
performance
Assumption residuals are
normally distributed
Discontinuities for single policies
cannot be properly modeled
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
44
22
Overall Findings
 For vanilla options, Least-Squares Monte Carlo works very well on both option price and
Greeks approximations
 For variable annuities, Least-Squares Monte Carlo works very well for liability FMV
estimation, but not so well for Greeks
 At a book level, the hedge performance using Least-Squares Monte Carlo fitted FMV
and Greeks was not as effective as Brute-Force Monte Carlo method
 The fast growth of GPU and cloud technology provides a speedy and low-cost solution
for VA
– Nested Brute-Force Monte Carlo calculations
– Testing and refining proxy approaches like LSCM to speed up nested stochastic
calculations
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
45
Bibliography
Longstaff, F and Schwartz, E.
Valuing American options by Simulation: A Simple Least-Squares Approach
Review of Financial Studies 14, pp537-548(2001)
Cathcart, M and Morrison, S.
Variable annuity economic capital: the least-squares Monte Carlo Approach
Life & Pension 2011
Stentoft, L
Convergence of the Least Square Monte Carlo Approach to American Option Valuation
Management Science,
Vol. 50, No. 9 (Sep., 2004)
Horig, M. & Leitschkis, M
Solvency II Proxy Modeling via Least Squares Monte Carlo
Milliman Research Report, 2012
Carrol, S & Hursey, C
Efficient Curve Fitting Techniques
Life & Conference and Exhibition, 2011
Aon Benfield Securities, Inc. | Annuity Solutions Group
Proprietary & Confidential | November 17, 2014
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23
11/28/2014
State-of-the-art
Hybrid Modeling
for Fixed Indexed Annuities
and Variable Annuities
Russell Goyder
Equity-Based Insurance Guarantees Conference
Chicago, November 17th 2014
Session 3A: 1530 – 1700 hours
© 2014 - FINCAD
Outline
1.
2.
3.
4.
5.
6.
7.
8.
Brief review of annuity modeling
Hybrid modeling challenges
Copula hybrid models
Contract representation
Analytic risk
Valuation compiler
Examples
Conclusion
Source
code
© 2014 - FINCAD
Parser
AST
Semantic
Analysis
IR
Code
generati
on
Progra
m
Executio
n
2
1
11/28/2014
Brief Review of
Annuity Modeling
© 2014 - FINCAD
3
The first annuities
1982
1959
1952
1912
First variable annuity
is issued by TIAACREF.
The Supreme Court
holds that variable
annuities are subject
to federal securities
regulation.
The Tax Equity and
Fiscal Responsibility
Act of 1982 allows
annuities to keep
their valuable taxdeferred status.
The Pennsylvania Company for
1759
Insurance on Lives and Granting
Annuities offers annuities to the
general public.
First annuity in America offered
by a Pennsylvania company to
Presbyterian ministers and
their families.
Sources: IRI Insight Issue 5 Volume 4 and www.limra.com
© 2014 - FINCAD
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2
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The first complex guarantees
1999
1997
1996
Total annuity assets
top $1 trillion.
Variable annuity
sales top $100
billion a year.
GMIB introduced.
1995
Annuity industry
sales top $100
billion for the year.
1984
1759 – 1982
The Tax Reform
Act of 1984
eliminates the
double taxation
of realized
capital gains of
separate
accounts.
Sources: IRI Insight Issue 5 Volume 4 and www.limra.com
© 2014 - FINCAD
5
Modern landscape
2012
2006
2005
2004
GMWB
introduced.
Indexed annuity
sales top $25
billion for the
year.
The Pension
Protection Act
of 2006
overhauls the
federal pension
plan.
Living benefits
make up 84%
of all North
American sales
2002
GMAB
introduced.
1984 – 1999
Sources: IRI Insight Issue 5 Volume 4 and www.limra.com
© 2014 - FINCAD
6
3
11/28/2014
Accumulation phase
Guaranteed
benefits are
functions of
several possible
end values
Premium P
(PR = Participation Rate)
© 2014 - FINCAD
7
GMxB features
Accumulation benefit:
rollover options
Death benefit
Accumulation
benefit
Income Benefit:
rollover options
Income benefit
© 2014 - FINCAD
8
4
11/28/2014
GMxB modeling literature
2000
2002
Milevsky + Posner
Windcliff et al
•
•
•
•
2004
2006
2008
2010
2012
Milevsky + Salisbury
van Haastrecht et al
Boyle + Hardy
Ballotta + Haberman
•
•
•
•
•
•
2014
Huang et al
Marshall et al
van Haastrecht et al
•
•
Coleman et al
Chen + Forsyth
Dai et al
Chen et al
Bauer et al
Ulm
•
•
•
Benhamou et al
Blamont + Sagoo
Kling et al
1990’s
•
•
•
Ravindran + Edelist (1996)
Benhamou et al
Blamont + Sagoo
•
•
Feng + Volkmer
Krayzler et al
© 2014 - FINCAD
9
Model and contract breakdown
• Sparse coverage
of the modelcontract matrix
• Very limited
modeling of
equity and rates
smiles
© 2014 - FINCAD
Mortality
Interest rates
Fund value
Basic
Determ.
Lognormal
Basic
Determ.
Lognormal
Basic
Determ.
Local vol
DeMoivre
Determ.
Lognormal
Stochastic
1f HJM
Lognormal
Basic
Hull White
Lognormal
Basic
Hull White
Merton JD
Basic
Hull White
Heston
Basic
Vasicek
Lognormal
Basic
Hull White 2f
Schoebel-Zhu
CIR
CIR
Heston
Stochastic
CIR
Lognormal
10
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Themes in the literature
Actuarial
Biometric emphasis
Stochastic mortality
Lognormal fund dynamics
Deterministic interest rates
Good coverage of contract features
Monte Carlo / practical pricing methods
Derivatives
Financial emphasis
Simple mortality
Smile models for fund
Interest rate models
Focus on GMAB and GMIB
Focus on (quasi) analytic solutions
© 2014 - FINCAD
11
Literature summary
• Many complex contract features
• Contract coverage is patchy
• Mortality risks treated independently
• Hybrid models for equity-rates exposure
• Each hybrid model is a research project
• Smile models are pretty rare
© 2014 - FINCAD
12
6
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Hybrid Modeling
Challenges
© 2014 - FINCAD
13
Why is hybrid modeling so hard?
Stochastic rates destroy equity calibrations
Careful analysis for consistent measure
Simulation headaches
Non-intuitive correlation
© 2014 - FINCAD
14
7
11/28/2014
To illustrate:
• Simple example
– Yet sufficient to illustrate the above challenges
– Lognormal equity, Hull White interest rates
© 2014 - FINCAD
15
Equity-rates example: calibration
Canonical Treatment
Forward:
is a martingale:
where
and
© 2014 - FINCAD
16
8
11/28/2014
Equity-rates example: calibration
Calibrate rates model parameters ܽ and ߪ௥(‫)ݐ‬
Price options via
where
For a given value of ߩ, solve for equity vol ߪௌ(‫)ݐ‬
© 2014 - FINCAD
17
Equity-rates example: valuation
Bring into common measure, say ܶ-forward
Given ‫ݔ‬௜~Φ(0,1) and ݅௧௛ short rate ‫ݎ‬௜,
© 2014 - FINCAD
18
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11/28/2014
Equity-rates example: valuation
Short rate
where
and
© 2014 - FINCAD
19
Equity-rates example: correlation
• Calibrate correlation if have quotes
• Rare, so often combination of intuition and
historical analysis
Exercise caution - given two functions
Not when ߩ = 0, but when
© 2014 - FINCAD
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Summary
• Hybrid models:
Require bespoke calibration techniques
Need significant quantitative research
Need bespoke software implementation
Contain confusing correlation parameters
Hybrid models are rare and low-fi
© 2014 - FINCAD
21
Our goals
Multi-factor marginals
Configure (not code)
arbitrary hybrid models
Mortality
Handle any number
of underlyings
Full coverage of
“model – contract”
matrix
© 2014 - FINCAD
Fund value
(SLV)
Copula
Withdrawal
Correlations that
make sense
Rates
(LMM)
Parallel
computation
and in-memory
caching
Analytic Risk
22
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Copula Hybrid Models
© 2014 - FINCAD
23
Model the forward directly?
© 2014 - FINCAD
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12
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Model the forward directly?
© 2014 - FINCAD
25
Decoupled equations
• Reuse existing calibrations
• Reuse existing marginal distribution generators
• Intuitive correlation
• ߩᇱ = 0 means independent observables
• What about a consistent measure?
© 2014 - FINCAD
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Problem
Anchored to time ܶ. Recover spot price at ܶ:
But for any other time t, introduce a drift ்߯ (‫)ݐ‬
where
• Drift correction tractable in this example
• In general it is not
© 2014 - FINCAD
27
Numeraire corrections
Define factor ߰ ் (‫)ݐ‬:
where
. No arbitrage if
where
© 2014 - FINCAD
28
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11/28/2014
Numeraire corrections
Substitute for ܵ(‫)ݐ‬:
1.
2.
3.
4.
5.
6.
7.
8.
Denominator is “run hybrid simulation ignoring drift issue”
Simple payoff converges very quickly
Need one correction factor per stock observation
Calculate as a preprocessing step
Scale each path by ߰ ்(‫)ݐ‬
Payoff independent
Model independent
Combine with compiler techniques
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2311740
© 2014 - FINCAD
29
Multiple currencies: prices
• Asset (foreign) currency ‫ܣ‬, numeraire (domestic) currency ‫ܤ‬
• ‫ ܤ‬worth of one ‫ ܣ‬is ܺ(‫)ݐ‬
Lognormal:
• Note use of ܺ(‫ )ݐ‬in correction for equity
• For ݊ currencies, choose one as numeraire, apply above to rest
© 2014 - FINCAD
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15
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Multiple currencies: rates
‫ݐ ݏ‬
‫ݑ‬
For rate from ‫ ݑ →ݐ‬fixed at ‫ݏ‬in currency ‫ܣ‬,
numeraire ܲ஻ (‫ݐ‬, ܶ)
so
© 2014 - FINCAD
31
Any model?
• Credit (default intensity), Inflation, Multi-curve
– By analogy with the above
• Jumps – implicitly through pdf
• Multi-factor models / discretized SDEs
– Clarity on marginal vs copula
• Large models (many marginals)
– Dimensionality reduction for correlation
• Correlation term structure
– Relate term to incremental correlation
© 2014 - FINCAD
Process ݅serial
correlation
32
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11/28/2014
Copula hybrids summary
• Model the forward, not spot, solves
• Solve the remaining measure issue
by
imposing Numeraire Corrections directly
• Goals checkup:
© 2014 - FINCAD
33
Contract
Representation
© 2014 - FINCAD
34
17
11/28/2014
Flows
Flow: obligation to deliver cash or physical asset
Leg: multiple flows
Swap: offsetting legs
Condition:
Delegate choice to rule
based on observables
Choice:
Right to receive flows
Or further rights
Convenience: Null, Remainder, Reweight…
http://www.fincad.com/derivatives-resources/white-papers/optimal-architecture.aspx
© 2014 - FINCAD
35
Observables
Flow anatomy:
Pay or receive
Observation
time
Accrual
Notional
Numeraire
Observable
Payment time
Core language elements for ܺ(‫)ݏ‬
• +-/*
• max, min
• if( C, A, B ) for condition C and results A and B
• and, or, <, <=, >, >=
• bind to observation date (time)
© 2014 - FINCAD
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Example observables
Account value: ‫)ݐ(ܣ‬
Ratchet: max( ‫ܣ‬௜)
{௧೔}
Rollup: 1 + ‫ ݎ‬௜‫ܣ‬௜
Participation rate r
஺
Return ‫ݕ‬௜ = ೔ − 1
஺೔షభ
Ratchet: ‫ݐ ܩ‬௜ = ‫ݐ ܩ‬௜ିଵ (1 + max ‫ݕݎ‬௜, 0 )
Accumulation benefit: max(‫ܣ‬௜, ‫ܩ‬௜)
© 2014 - FINCAD
37
Example contracts
• GMAB: flow( USD, ‫ݐ‬, max ‫ ݐ ܣ‬, ‫ॴ ݐ ܩ‬ఛவ௧)
• GMIB(‫)ݐ‬: leg( ‫ݐ‬, max ‫ ݐ ܣ‬, ‫ ݐ ܩ‬max ܴ ‫ݐ‬௜ , ݇ ॴఛவ௧೔)
• GMIB with rollover option:
– choice( GMIB ‫ݐ‬ଵ , choice GMIB ‫ݐ‬ଶ , … )
• GMDB: leg( ‫ݏ‬, max ‫ݐ ܣ‬௜ , ‫ݐ ܩ‬௜ ॴ௧೔ష భழఛஸ௧೔, )
• GMWB:
– Rational policyholder
• ‫ܥ‬௡ = choice( flow ‫ݐ‬௡ , ‫ݐ ܩݓ‬௡
+ scale GMIB ‫ݐ‬௡ , 1 − ‫ ݓ‬, GMIB ‫ݐ‬௡ )
• ‫ܥ‬௡ିଵ = choice flow ‫ݐ‬௡ିଵ, ‫ݐ ܩݓ‬௡ିଵ
– Policyholder behavior model
+ scale(‫ܥ‬௡ , 1 − ‫ ݓ‬, ‫ܥ‬௡ )
• No choices, model the fraction of fund units remaining
© 2014 - FINCAD
38
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11/28/2014
Analytic Risk
© 2014 - FINCAD
39
Fast, exact first-order exposures
• Portfolio of 250 trades
• 80% 10y Libor swaps
• Remainder:
– CDS
– USD-EUR xccy swaps
– Swaptions, options,
• Desktop PC, core i7 CPU
• Sensitive to >400 quotes:
– OIS, Libor, basis and xccy
swaps, USD and EUR vol cube,
equity vols, survival, FX spot …
• 600x speed-up
• Typically 10-10,000x
http://www.fincad.com/pdfs/F3 Universal Risk Technology.pdf
© 2014 - FINCAD
40
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Valuation Compiler
© 2014 - FINCAD
41
Compilation process
Source
code
Parser
AST
Semantic
Analysis
IR
Code
generation
Program
Execution
Rules
int f(int x) {
int result = (x / 42);
return result;
}
clang –Weverything -pthread –fPIC code.cpp
© 2014 - FINCAD
42
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Compilation process
Source
code
Parser
AST
Semantic
Analysis
IR
Code
generation
Program
Execution
Rules
llvm ir output:
.text
; ModuleID = 'test.c'
.globl _Z1fi
target datalayout = "e-p:64:64:64-i1:8:8-i8:8:8-i16:16:16-i32:32:32-i64:64:64-f32:32:32.align 16, 0x90
f64:64:64-v64:64:64-v128:128:128-a0:0:64-s0:64:64-f80:128:128-n8:16:32:64-S128"
.type
_Z1fi,@function
target triple = "x86_64-unknown-freebsd10.0"
_Z1fi:
; Function Attrs: nounwind
.cfi_startproc
define i32 @f(i32 %x) #0 {
# BB#0:
%1 = alloca i32, align 4
pushq
%rbp
%result = alloca i32, align 4
.Ltmp2:
store i32 %x, i32* %1, align 4
.cfi_def_cfa_offset 16
%2 = load i32* %1, align 4
.Ltmp3:
%3 = sdiv i32 %2, 42
.cfi_offset %rbp, -16
i32 %3,
i32*
%result, align 4
`-FunctionDecl 0x5aeab50 <test.cc:1:1, store
line:4:1>
f 'int
(int)'
movq
%rsp, %rbp
%4 = load
i32* %result, align 4
|-ParmVarDecl 0x5aeaa90 <line:1:7, col:11>
x 'int'
.Ltmp4:
ret i32 %4
`-CompoundStmt 0x5aead88 <col:14, line:4:1>
.cfi_def_cfa_register %rbp
}
|-DeclStmt 0x5aead10 <line:2:3, col:24>
movl
$42, %eax "noattributes
= { nounwind "less-precise-fpmad"="false" "no-frame-pointer-elim"="false"
| `-VarDecl 0x5aeac10 <col:3, col:23>
result#0
'int'
movl
%edi, -4(%rbp)
frame-pointer-elim-non-leaf"="false"
"no-infs-fp-math"="false" "no-nans-fp-math"="false"
|
`-ParenExpr 0x5aeacf0 <col:16,
col:23> 'int'
movl
-4(%rbp), %edi
"use-soft-float"="false" }
|
`-BinaryOperator 0x5aeacc8 "unsafe-fp-math"="false"
<col:17, col:21> 'int' '/'
movl
%eax, -12(%rbp)
|
|-ImplicitCastExpr 0x5aeacb0 <col:17> 'int' <LValueToRValue>
movl
%edi, %eax
|
| `-DeclRefExpr 0x5aeac68 <col:17> 'int' lvalue ParmVar 0x5aeaa90 'x' 'int'
cltd
|
`-IntegerLiteral 0x5aeac90 <col:21> 'int' 42
movl
-12(%rbp), %edi
`-ReturnStmt 0x5aead68 <line:3:3, col:10>
idivl
%edi
`-ImplicitCastExpr 0x5aead50 <col:10> 'int' <LValueToRValue>
movl
%eax, -8(%rbp)
`-DeclRefExpr 0x5aead28 <col:10> 'int' lvalue Var 0x5aeac10 'result' 'int'
© 2014 - FINCAD
43
Valuation compiler
Source
code
Parser
AST
Semantic
Analysis
IR
Code
generation
Program
Execution
Rules
flow( 1y3m, EUR, min( C, S(1y)*Libor3m ) )
Observable
Process
S
Black
Libor3m
Hull White 1f
<S, Libor3m> = -0.2
© 2014 - FINCAD
44
22
11/28/2014
Valuation compiler
Source
code
Parser
AST
Semantic
Analysis
IR
Code
generation
Program
Execution
Rules
- min
- constant
- flow
| C
| 1y3m
- multiply
| EUR
- multiply
- min
- simulate
| C
| S
- multiply
| 1.0
- observe
- correction
| S
- simulate
| 1y
| Libor3m
| Libor3m
| 1.0
- numeraire
Payoff operations
Numeraire
correction
lognormal
HW 1f
Constants
copula
U(0,1)
© 2014 - FINCAD
45
Valuation compiler
Source
code
Parser
AST
Semantic
Analysis
IR
Code
generation
Program
Execution
Rules
Observable
Process
EUR Stock 1
Stoch Local Vol
EUR Stock N
CGMYe
USD Stock 1
Stoch Local Vol
USD Stock M
Norm. inverse Gaussian
EURUSD
Stoch Local Vol
USD Libor 3m
Quad. Gaussian 3f
Euribor 3m
LMM
Counterparty survival
Hull White 1f
© 2014 - FINCAD
<EUR S1, EUR SN> = -0.5
<EUR SN, USD SM> = 0.75
<EUR S1, Libor3m> = -0.2
<USD Libor, Euribor> = 0.8
46
23
11/28/2014
Valuation compiler
Source
code
Rules
Parser
Input
1. Contract details
2. Model
• Marginals
• Copula
3. Valuation methodology
• Eg, Simulation
• Number of paths
4. Desired outputs
• Value, Risk
• Histogram, cash flows
© 2014 - FINCAD
AST
Semantic
Analysis
IR
Code
generation
Program
Execution
Compiler
1. Parallel computation
• Multi-threading
• Multi-process
2. Numeraire Corrections
3. Analytic Risk
4. Cross-platform support
47
Examples
© 2014 - FINCAD
48
24
11/28/2014
Example – GMAB paths
•
•
•
For a given contract,
compare models path
by path
Ratchet guarantee
with participation rate
Four one-factor
models, same
Brownian sample for
each
– Lognormal as
baseline
– Single parameter
local vol
– Two jump-diffusions
•
No rates exposure
© 2014 - FINCAD
49
Example – GMAB histogram
© 2014 - FINCAD
50
25
11/28/2014
Example – rollover options
•
•
•
•
•
•
Ratchet guarantee with
80% participation rate as
before
Notional is guarantee
level at 10y, coupon is
greater of 5y swap rate
at 10y and a fixed strike
Mortality assumption: ΓGompertz curve*
10 year accumulation
phase with death
guarantee
Subsequently, 30 year
annuity stream
Option to start annuity
each year from year 10
to 20
* http://paa2012.princeton.edu/papers/121013
© 2014 - FINCAD
51
Correlation
© 2014 - FINCAD
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26
11/28/2014
Convergence
© 2014 - FINCAD
53
Conclusion
Hybrid modeling is hard
Numeraire
corrections
Copula hybrids
Flows and observables
Compiler ideas
© 2014 - FINCAD
Copula hybrid
modeling
Flexibility
Model-contract
combinatorics
54
27
11/28/2014
State-of-the-art
Hybrid Modeling
for Fixed Indexed Annuities
and Variable Annuities
Russell Goyder
Equity-Based Insurance Guarantees Conference
Chicago, November 17th 2014
© 2014 - FINCAD
References
Ravindran, K. & W. Edelist (1996). Deriving benefits from death in frontiers. In A. Konishi and R. E. Dattatreya (Eds.), Derivatives: State-of-the-Art Models,
Valuation, Strategies and Products. New York: McGraw Hill.
Milevsky, M.A. and S.E. Posner (2001), The Titanic Option: Valuation of Guaranteed Minimum Death Benefit in Variable Annuities and Mutual Funds, Journal
of Risk and Insurance, Vol. 29(3), pg. 299-318.
Windcliff, H., P.A. Forsyth, P.A. and K.R. Vetzal (2001). Valuation of Segregated Funds: Shout Options with Maturity Extensions, Insurance: Mathematics and
Economics, Vol. 29(2), pg. 1-21.
(UK, forshadowing result that insurance industry was mispricing complex options) (stock disappears somewhere?)
Boyle, P.P. and M. Hardy (2003), Guaranteed Annuity Options, ASTIN Bulletin, Vol. 33(2), pg. 125-152.
Ballotta, L. and Habaerman, S. (2003) Guaranteed annuity conversion options and their valuation. www.casact.org.
Currie I.D., Durban, M. and Eilers, P.H.C. (2004) “Smoothing and forecasting mortality rates,” Statistical Modelling, 4: 279-298.
Milevsky, M.A. and T.S. Salisbury (2006), Financial Valuation of Guaranteed Minimum Withdrawal Benefits, Insurance: Mathematics and E conomics, Vol. 38,
pg. 21-38.
Coleman, T.F., Y. Li and M.C. Patron (2006), Hedging Guarantees in Variable Annuities Under Both Equity and Interest Rate Risk, Insurance: Mathematics
and Economics, Vol. 38(2), pg. 215-228.
Chen, Z. and P.A. Forsyth (2008), A Numerical Scheme for the Impulse Control Formulation for Pricing Variable Annuities with a Guaranteed Minimum
Withdrawal Benefit (GMWB), Numerische Mathematik, Vol. 109, pg. 535-569
Dai, M., Y.K. Kwok and J. Zong (2008), Guaranteed Minimum Withdrawal Benefit in Variable Annuities, Mathematical Finance, Vol 18(4), pg. 595-611.
Bauer, D., A. Kling and J. Russ (2008). A universal pricing framework for guaranteed minimum benefits in variable annuities. Astin Bulletin, vol. 38(2):621–
651.
Chen, Z., K. Vetzal and P. Forsyth (2008). The effect of modelling parameters on the value of GMWB guarantees. Insurance: Mathematics and Economi cs,
vol. 43(1):165–173.
© 2014 - FINCAD
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28
11/28/2014
References
Ulm, E.: Analytic solution for return of premium and rollup guaranteed minimum death benefit options under some simple mortal ity laws, ASTIN Bulletin,
38(2), 543-563.
Benhamou, Eric and Gauthier, Pierre, Impact of Stochastic Interest Rates and Stochastic Volatility on Variable Annuities. Paris December 2009 Finance
International Meeting AFFI - EUROFIDAI.
D. Blamont and P. Sagoo. Pricing and hedging of variable annuities. Life & Pensions, pages 39-44, Feb 2009.
Marshall, G., Hardy, M. and Saunders, D.: Valuation of a guaranteed minimum income benefit, North American Actuarial Journal, 14(1), 38-58.
van Haastrecht, A., Plat, R. and Pelsser, A. A. J., (2010), Valuation of guaranteed annuity options using a stochastic volatility model for equity
prices, Insurance: Mathematics and Economics, 47, issue 3, p. 266-277.
van Haastrecht, Alexander and Lord, Roger and Pelsser, Antoon and Schrager, David, Pricing Long-Maturity Equity and FX Derivatives with Stochastic
Interest Rates and Stochastic Volatility January 10, 2005). Insurance: Mathematics and Economics, Vol. 45, No. 3, 2009. Available at SSRN:
http://ssrn.com/abstract=1125590
Ng, A.C. and J.S. Li (2011), Valuing Variable Annuity Guarantees with the Multivariate Esscher Transform, Insurance: Mathematics and Economics, Vol. 49(2),
pg. 393-400.
Bacinello, A.R., P. Millossovich, A. Olivieri and E. Pitacco (2011), Variable Annuities: A Unifying Valuation Approach, Insurance: Mathematics and Economics,
Vol. 49(2), pg. 285-297.
Kling, A., F. Ruez and J. Russ (2011), The impact of Stochastic Volatility on Pricing, Hedging, and Hedge Efficiency of Withdrawal Benefit Guarantees in
Variable Annuities, Astin Bulletin, Vol. 41 (2), pg. 511-545
Feng, R. and H.W. Volkmer (2012), Analytical Calculation of Risk Measures for Variable Annuity Guaranteed Benefits, Insurance: Mathematics and
Economics, Vol. 51(4), pg. 636-648.
Krayzler, Mikhail and Zagst, Rudi and Brunner, Bernhard, Closed-Form Solutions for Guaranteed Minimum Accumulation Benefits (November 30, 2012).
Available at SSRN: http://ssrn.com/abstract=2425801
Huang, H., Milevsky, M.A. and Salisbury, T.S. Optimal Initiation of a GLWB in a Variable Annuity: No Arbitrage Approach (arXiv:1304.1821v1 [q-fin.PM] 5 Apr
2013)
© 2014 - FINCAD
57
29
Understanding and
Managing Policyholder
Behavior Risks
Equity-Based Insurance Guarantees Conference
Session 3B, 3:30-5:00pm, 17 November 2014
Ben Neff, FSA
GGY AXIS
Timothy Paris, FSA, MAAA
Ruark Insurance Advisors, Inc.
Overview of Recent
Industry Experience
2
2
1
Actual / Expected
VA Mortality
Less rich
More rich
Death benefits
Living benefits
Aggregate
3
3
Withdrawal Frequency
VA Partial Withdrawal Frequency
<50
50-59
GLWB Non-Qual
60-64
65-69
Attained Age
GLWB Qual
No LB Non-Qual
4
70-79
80+
No LB Qual
4
2
Withdrawal Frequency
VA Partial Withdrawal Frequency
1
2
3
4
Duration
5
6
7+
5
5
Withdrawal Frequency
VA Partial Withdrawal Frequency
<50
50-59
60-64
65-69
70-79
Attained Age
Commencement Non-Qual
Commencement Qual
Continuation Non-Qual
80+
Continuation Qual
6
6
3
Annual Withdrawal as % of Account Value
VA Partial Withdrawal Amounts
<50
50-59
60-64
65-69
70-79
Attained Age
GLWB
No Living Benefits
80+
7
7
VA Partial Withdrawal Amounts
Excess
Less Than Full
Full
8
8
4
VA Partial Withdrawal Amounts
Younger Ages
Less Than Full
Excess
Full
9
9
VA Partial Withdrawal Amounts
Older Ages
Excess
Less Than Full
Full
10
10
5
Surrender Rate
VA Surrenders
7 or more
6
5
4
3
2
1
0
-1
-2
Years Remaining in Surrender Charge Period
-3 or
more
11
11
Surrender Rate
VA Surrenders by Company
7
6
5
4
3
2
1
0
Years Remaining in Surrender Charge Period
12
-1
-2
-3
12
6
Surrender Rate
VA Surrenders
Years Remaining in Surrender Charge Period
2008
2009
2010
2011
2012
2013
13
13
Surrender Rate
VA Surrenders
7 or more
6
5
4
3
2
1
0
-1
-2
Years Remaining in Surrender Charge Period
GMIB
GLWB
GMWB
14
GMAB
-3 or
more
None
14
7
Surrender Rate
VA and FIA Surrenders
7 or more
6
5
4
3
2
1
0
Years Remaining in Surrender Charge Period
VA GLWB
VA No LB
FIA GLWB
-1
-2
-3 or
more
FIA No LB
15
15
VA Surrenders
ITM 100+%
ITM 50 - 100%
Surrender Rate
ITM 25 - 50%
ITM 5 - 25%
ATM
OTM 5 - 25%
OTM 25+%
7 or more
6
5
4
3
2
1
0
Years Remaining in Surrender Charge Period
16
-1
-2
-3 or
more
16
8
Surrender Rate
VA Surrenders – GMIB Shock Lapse
1Q 2Q 3Q 4Q 1Q 2Q 3Q 4Q 1Q 2Q 3Q 4Q 1Q 2Q 3Q 4Q 1Q 2Q 3Q 4Q 1Q 2Q
08 08 08 08 09 09 09 09 10 10 10 10 11 11 11 11 12 12 12 12 13 13
Calendar Quarter
<25% ITM
25%-50% ITM
50%-100% ITM
17
17
Surrender Rate
VA Surrenders and Partial
Withdrawals Interaction
7 or more
6
5
4
3
2
1
0
-1
-2
Years Remaining in Surrender Charge Period
No Prior WDs
LT Full WDs
Full WDs
Excess WDs
18
-3 or
more
18
9
Analysis and
Risk Management
19
19
Surrenders
20
20
20
10
Assumptions
PrinciplesBased
Framework
Experience
21
Behavior
21
Cohort
Benchmark
22
22
11
23
23
24
24
12
25
25
Surrenders for living
benefit guarantees
Repeated adverse
deviation
“Cat” protection for
assumption changes
26
26
13
VA Modeling
Considerations &
Case Study
27
27
Agenda
 VA modeling considerations
 Case Study
• Product definition
• Inforce and assumptions
• Discussion of Results
28
28
14
Evolving Model Complexity
Interest Markets
Faster
chips
Economic
Enviroment
Grid
Equity Markets
Technology
Innovation
Cloud
Stochastic Analysiss
Governance
Demographics and
Policyholder Behavior
AG43
Actuarial
Models
Reporting
GAAP
Regulation
Risk Management
SOX
Reinsurance
Product
Innovation and
Pricing
C3P2
Compliance
Hedging and
ALM
Solvency
CCAR
MAR
29
29
VA Modeling Requirements




Economic Environment
Product capture
Policyholder behavior
Risk Management
• Hedging and ALM
• Reinsurance
 US Stat and US GAAP financials
• AG43, C3P2
• FAS 97, FAS 133, SOP 03-1
30
30
15
Economic Environment
 Starting Economic Scenario
• Interest rate markets, equity markets (growth,
div, vol), inflation, other variables
 Scenario Generation
• Risk Neutral

Hedging, FAS 133
• Real World



AG43, C3P2
SOP 03-1
CCAR
31
31
Product Characteristics
 Policyholder investment options
• Managed funds

Equity, Bond, Dynamic
• General account
 Guarantees
• Benefit base – Roll-up, ratchet, reset, etc.
• Benefit types – Death, income, accumulation,
withdrawal
32
32
16
Policyholder Behavior




Fund allocation
Lapse
Partial withdrawal
Annuitization
 Needs to be dynamic
 Capture interaction between economic
environment, product, policyholder
33
33
Risk Management
 ALM and hedging
• Market risk focused
• Projection of risk metrics – nested stochastic
simulation
• Execution of a hedging strategy
 Reinsurance
• Insurance risk focused – mortality, lapse, etc.
• Treaty level features can introduce modeling
challenges – limits, thresholds, etc.
 Potential impact on reserve and capital
34
34
17
US Statutory Reserve and Capital
AG430
C3P20
AG433
C3P23
Liabilities
AG435
C3P25
Liabilities
Liabilities
Scenario
set
Block
Reinvestment
Strategies
Scenario
set
Scenario
set
Block
Block
Assets
Reinvestment
Strategies
Standard
Scenario
Reinvestment
Strategies
Assets
Standard
Scenario
Standard
Scenario
Stoch
Valuation
Date
Year 1
Assets
Stoch
Stoch
Year 2
Year 3
Year 4
Year 5
35
35
US GAAP Calculations
DAC0
DAC3
DAC5
Amort0 (EGP0;K0)
Amort3 (EGP3;K3)
Amort5 (EGP5;K5)
SOP 03 – 1
SOP 03 – 1
SOP 03 – 1
FAS 133
FAS 133
FAS 133
FAS 97
FAS 97
FAS 97
Valuation
Date
Year 1
Year 2
Year
Year 33
Year
Year 44
Year
Year55
History
36
36
18
Consolidated Projections
Results
Cube
Results
Cube
Results
Cube
Results
Cube
Results
Cube
Results
Cube
Results
Cube
Results
Cube
Results
Cube
Results
Cube
Results
Cube
Results
Cube
37
37
38
38
Case Study
19
Product
 VA with GLWB
• 5% roll-up to age 85
• GLWB% varies with attained age
 Fees:
• 1.4% M&E fees
• 1.2% Rider fees
 7 year surrender charge schedule
 Expense:
• 1.2% Fund level expense
39
39
Inforce
 Seasoned block of VA with GMWB
•
•
•
•
•
Issue age 55 – 75
Average age 65
10B of AV
100k policies, average size 100k
Guarantee base ~ 120% of AV
40
40
20
Assumptions
 Asset allocation – Fixed across major
indices and cash
 Mortality – 85% of Ruark mortality table
 Partial withdrawal
• Increasing frequency based on att. age
• Amount is 90% of GLWB
 Dynamic lapse
• Rate = Base Rates (A) * Dynamic Factor
• Dynamic Factor = B ^ (ITM * C)
41
41
Discussion of Results
42
42
21
Reserve (Lapse, Guarantee Base)
 Point in time valuation
 Deterministic time 0 shocks of:
• Fixed lapse assumption
• Fund value
43
43
AG43 - Excess Reserve VS. Guarantee Base
3,000,000
Lapse = 7%
Excess Reserve (Thousands)
2,500,000
Lapse = 5%
Lapse = 3%
2,000,000
Lapse = 1%
1,500,000
1,000,000
500,000
0%
10%
20%
30%
40%
50%
60%
70%
(Guarantee Base - AV) / AV
44
44
22
Lapse
 Random variable
 Approximate via a function
• Rate = Base Rates (A) * Dynamic Factor
• Dynamic Factor = B ^ (ITM * C)
 What values of A, B, C?
• Calibration based on average historical data?
• What if calibration is incomplete or incorrect?
• What if we end up in a the tail of the
distribution?
45
45
Lapse = A * B ^ (ITM * C)
25.00%
A = 8%, B = .85, C = 10
A = 12%, B = .85, C = 35
20.00%
A = 8%, B = .9, C = 10
A = 10%, B = .9, C = 35
15.00%
Lapse
A = 10%, B = .8, C = 20
A = 9%, B = .85, C = 20
10.00%
5.00%
0.00%
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
Moneyness
46
46
23
Lapse = A * B ^ (ITM * C)
10.00%
9.00%
A = 8%, B = .85, C = 10
8.00%
7.00%
Lapse
6.00%
5.00%
4.00%
3.00%
2.00%
1.00%
0.00%
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
Moneyness
47
47
AG43 - Total Reserve VS. Guarantee Base
3,000,000
Lapse = 7%
Excess Reserve (Thousands)
2,500,000
Lapse = 5%
Lapse = 3%
Lapse = 1%
2,000,000
Lapse = Dynamic
1,500,000
1,000,000
500,000
0%
10%
20%
30%
40%
50%
60%
70%
(Guarantee Base - AV) / AV
48
48
24
Financial Projection
 12 quarter projection
• Recalculate Statutory reserves each quarter
 Economic Scenario
• Shock equity markets down 20% over 6 moths
 Lapse – Outer vs. Inner Loop Assumption
• Base Assumption (A = 8, B = .85, C=10)
• Shock Assumption (A = 9, B=.85, C=10)
49
49
Lapse = A * B ^ (ITM * C)
10.00%
A = 8%, B = .85, C = 10
9.00%
A = 9%, B = .85, C = 20
8.00%
7.00%
Lapse
6.00%
5.00%
4.00%
3.00%
2.00%
1.00%
0.00%
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
Moneyness
50
50
25
Projection Results
 Case 1 – Baseline
 Case 2 – Outer loop lapse sensitivity
 Case 3 – Inner loop lapse sensitivity
• AG43 lapse assumption change in Q4 2014
 Metrics:
• Projected lapse along outer loop
• Projected excess reserve
51
51
Case 1 - Baseline
YE
2012
Q1
2013
Q2
2013
Q3
2013
Q4
2013
Q1
2014
Q2
2014
Q3
2014
Q4
2014
Fund 10,005
9,168
8,458
8,301
8,155
8,012
7,874
7,742
7,615
Millions
Guarantee
Base 12,342 12,331 12,297 12,280 12,267 12,253 12,243 12,237 12,235
Outer Loop
5.5% 4.6% 3.8% 3.7% 3.5% 3.4% 3.2% 3.1%
Lapse
Excess
Reserve
Change in
Excess
Reserve
259
408
607
637
666
677
705
733
761
149
199
30
29
10
28
28
28
52
52
26
Case 2 – Outer Loop Sensitivity*
YE
2012
Q1
2013
Q2
2013
Q3
2013
Q4
2013
Q1
2014
Q2
2014
Q3
2014
Q4
2014
Fund 10,005
9,193
8,516
8,395
8,285
8,176
8,071
7,971
7,874
Millions
Guarantee
Base 12,342 12,364 12,383 12,420 12,462 12,503 12,549 12,598 12,652
Outer Loop
Lapse
3.7% 2.6% 1.8% 1.7% 1.6% 1.4% 1.3% 1.2%
Excess
Reserve
Change in
Excess
Reserve
259
Change VS
Baseline
409
611
644
677
691
723
754
787
150
202
33
33
14
32
32
32
1
3
3
3
3
4
4
4
*Outer loop lapses based on shock assumption
53
53
Case 3 – Inner Loop Sensitivity*
YE
2012
Q1
2013
Q2
2013
Q3
2013
Q4
2013
Q1
2014
Q2
2014
Q3
2014
Q4
2014
Fund 10,005
9,193
8,516
8,395
8,285
8,176
8,071
7,971
7,874
Millions
Guarantee
Base 12,342 12,364 12,383 12,420 12,462 12,503 12,549 12,598 12,652
Outer Loop
Lapse
3.7% 2.6% 1.8% 1.7% 1.6% 1.4% 1.3% 1.2%
Excess
Reserve
Change in
Excess
Reserve
Change VS
Case 1
259
409
611
644
677
691
723
754
913
150
202
33
33
14
32
32
159
-
-
-
-
-
-
-
127
*Inner loop lapse assumption updated to shocked assumption in Q4 2014
54
54
27
Thank You!
Ben Neff, FSA
GGY AXIS
[email protected]
416.250.2568
Timothy Paris, FSA, MAAA
Ruark Insurance Advisors, Inc.
[email protected]
860.866.7786
55
55
28
10th Annual Equity Based Insurance Guarantee Conference (Chicago)
0830 – 1000 hours, 18 November 2014
Questions for Panel
1) General
a. Can you please give a summary of your background and the background of your
company (as it relates to the business it writes)?
b. How many people support the CRO function?
c. How long has the position being in existence in your company and how long have you
been in it?
d. What has been the biggest one-off challenge for you in the capacity of a CRO that you
have faced thus far (does not have to be VA/FIA related)?
2) Products and Risks
a.
What types of VA/FIA products does your company write? E.g GMWB/GMIB/FIA annual
crediting cap strategy that can be annuitized as a WB etc.
b. What is the contribution of the VA/FIA related business to your rest of the book of
business in terms of size and the risks it introduces the firm to?
c. What kind of distribution channel does your company use for the products in 2a.
d. What keeps you up at night in regards to the risks the company takes on in selling the
products identified in 2a?
e. What are the top 3 ongoing challenges in dealing with these products – in the order of
magnitude?
f.
How different are the rating agencies/regulators etc. opinion of your answers in 2e)?
g. How has the withdrawal of certain players in the market affected the way you conduct
your business?
h. Do you work with senior management and the board to identify black-swan events?
What do they tend to be and how often are these identified events updated/reviewed
to take into consideration current environment?
3) Takeaways
a. What is it that you know now which you wish you knew when you started your job as a
CRO?
b. If you could start all over again, what would you do different?
c. What would be one key take away you would like participants of the conference to
leave with from this panel discussion?
11/28/2014
Research
Anshul Pradhan
+1 212 412 3681
[email protected]
US Rates Outlook
10th Annual Equity Based Insurance Guarantees Conference (Chicago)
18 November 2014: 1030 – 1115 hours
PLEASE SEE ANALYST CERTIFICATIONS AND IMPORTANT DISCLOSURES BEGINNING ON PAGE 31
Macro environment in 2014
• Consensus view that yields will rise following the taper has been proven
wrong.
• Relentless flattening of the yield curve, as the long end has been well
supported data but the start of the hiking cycle has been pegged
• Volatility has declined across asset classes globally; e.g. US 3m10y at 70abpv
(pre-crisis low : 57apbv). Both FX and Equity vol are also close to historical
This
lows
economic backdrop has kept the front end pegged,
Mixed data amid steady message from the Fed, has kept
but longer dated yields have rallied relentlessly
volatility low across asset classes
7.0
3.5
6.0
3.0
250
80%
70%
200
60%
2.5
5.0
2.0
50%
150
4.0
1.5
40%
3.0
100
1.0
2.0
30%
0.5
1.0
20%
50
0.0
0.0
Dec-04
Dec-06
Dec-08
5Y5Y swap yields, %
Dec-10
Dec-12
10%
-0.5
Dec-14
0
Jul-04
5s30s Treasury Curve, %, rhs
Jul-06
Jul-08
USD 10y, lhs
Jul-10
S&P, rhs
Jul-12
0%
Jul-14
FX, rhs
Source: Bloomberg, Barclays Research
2
1
11/28/2014
Why have long dated yields declined this year?
3
Decomposition of long dated yields into expectations and
term premium
Driven by a decline in expectations of real neutral rate
and term premium
2.5%
2.0%
5y5y swap yields have declined significantly
since the beginning of the year
1.5%
2.1%
1.8%
1.3%
1.0%
6.0
0.6%
0.5%
5.5
5.0
0.0%
4.5
-0.5%
4.0
3.5
Expecte Real Policy Expected Inflation
Rate
Dec-13
Nov-14
0.0%
Term Premium
0.0%
3.0
-0.1%
2.5
2.0
Dec-09
2.3%
-0.2%
Dec-10
Dec-11
Dec-12
Dec-13
Dec-14
-0.2%
-0.3%
-0.4%
5y5y swap rates, %
-0.5%
-0.5%
-0.6%
-0.6%
-0.7%
Expecte Real Policy Expected Inflation
Rate
Source: Federal Reserve, Barclays Research
Term Premium
4
2
11/28/2014
Growth forecasts have again turned out to be too optimistic
Growth forecasts have been pared back in the US
Not just in the US but globally as well
World GDP Forecasts
US GDP Forecasts
3.5
3.3
3.1
2.9
2.7
2.5
2.3
2.1
1.9
1.7
1.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
10
11
11
12
12
13
13
14
14
15
10
16
11
11
12
12
13
13
14
14
15
16
Source: Bloomberg, Barclays Research
5
Underlying GDP growth hasn’t shown any signs of acceleration
Underlying GDP in the 2-2.5% range
Q3 15 print was not as strong as suggested by the headline number
Contribution to real GDP
Personal Consumption
Non-Residential FI
Residential FI
Government-Defense
3.0
2.5
2.0
1.5
1.0
0.5
0.0
Sep-09
Sep-10
Sep-11
Sep-12
Sep-13
Sep-14
Q3-14
1.2
0.7
0.1
0.7
Q2-14
1.8
1.2
0.3
0.0
Q1-14
0.8
0.2
-0.2
-0.2
2013
1.9
0.6
0.2
-0.3
2012
1.3
0.4
0.4
-0.3
2011
1.0
1.0
0.1
-0.2
Government-Other
Net Exports
Change in inventories
0.2
1.3
-0.6
0.3
-0.3
1.4
0.0
-1.7
-1.2
-0.1
0.3
0.5
-0.1
0.3
-0.5
-0.4
0.0
0.2
Total
Final sales
Domestic Final sales
Domestic Pvt. Final Sales
3.5
4.1
2.8
2.0
4.6
3.2
3.5
3.2
-2.1
-1.0
0.7
0.9
3.1
2.6
2.3
2.7
1.6
2.1
1.8
2.2
1.7
1.5
1.5
2.2
Domestic Private Final Sales, 4Q MA
Source: CBO, Haver Analytics, Barclays research.
6
3
11/28/2014
Central banks globally are missing their inflation targets
Long term inflation expectations in DM have
been falling over the past year
Inflation is expected to remain below CB targets
Inflation (%)
Current vs 1 yr ago and next year Inflation expectation
3.5
%
3.5
3.0
3.0
UK BE (RPI):2.73
2.5
2.5
2.0
1.5
2.0
1.0
1.5
USBE 1.92
France BE: 1.18
0.5
Germany BE:
1.13
1.0
10
0.0
EU
US
Japan
UK
Canada Switzerland Sweden
Denmark
-0.5
Sep-13
Sep-14
Q3-15*
New
Zealand
11
12
13
14
Australia
UK BE 10y
Eur BE 10y
France BE 10y
USBE 10y
Inflation Target
Q2-15* is yoy quarterly average inflation forecasts (Core PCE for the US, CPI for all other countries)
Realized inflation forecasts are yoy core PCE for the US, core CPI for EU,UK and CPI for others
Source: Bloomberg, Barclays Research
7
Global term premia has compressed to unprecedented lows
Long dated yields have declined globally
Term Premium has compressed to historical lows
%
2.0
1.5
1.0
0.5
0.0
-0.5
-1.0
Jan-99
Jan-01
Jan-03
Jan-05
Recession
Jan-07
Jan-09
Jan-11
Term Premium
Jan-13
Source: Bloomberg, Barclays Research
8
4
11/28/2014
Rate volatility has declined to historical lows
Global rate vol close to pre recession lows
Dispersion of economic forecasts has declined
3.5
3.0
5
2.5
4
3
2.0
2
1.5
1
1.0
0
0.5
-1
0.0
Dec-66
-2
-3
-4
Nov-02
Dec-74
Dec-82
Dec-90
Dec-98
Dec-06
Dec-14
250D Realized Vol, 10y Tsy, %
Nov-04
Nov-06
Nov-08
Nov-10
Nov-12
Nov-14
1y10y swaption vol, %
PC1 of US, UK, Eur rate vol
Dispersion of real GDP growth forecasts (75/ 25%ile), 4Q MA
Source: Barclays Research
9
Negative correlation with equities has increased the perceived
diversification benefit in owning Treasuries
Stock-bond Return correlation has recently been negative
Correlation in close to the historical lows
1.00
0.6
Daily Tsy
Index Price
Return, %
2014
0.4
0.75
0.50
0.25
0.2
0.00
0.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
-0.25
-0.2
-0.50
-0.4
-0.75
-0.6
-1.00
Sep-94
Daily S&P Price Return, %
Sep-98
Sep-02
Sep-06
Sep-10
Sep-14
Stock-Bond Correlation, %
Source: Barclays Research
10
5
11/28/2014
Key Takeaways
Long dated yields have declined sharply, not just in the US but globally
Neutral rate expectations have declined along with growth forecasts
Inflation continues to remain stubbornly below Central Bank Targets
Term Premia has compressed to unprecedented lows
Volatility is close to historical lows given low dispersion in growth forecasts
Negative correlation with equities has increased the diversification benefit
11
What is the outlook for monetary policy?
12
6
11/28/2014
Monetary policy has been extremely accommodative by
historical standards
Fed’s Balance Sheet has expanded significantly
Fed funds rate has been stuck at the ZLB since 2008
10.0
5,000
8.0
30%
Current
9.0
25%
4,000
7.0
20%
3,000
6.0
15%
5.0
2,000
4.0
10%
3.0
1,000
2.0
1.0
5%
0
0.0
Dec-89
Dec-94
Dec-99
Dec-04
Dec-09
0%
04
Dec-14
06
Fed Funds Forecast, %
08
10
12
14
16
18
20
Tsys held in SOMA, $bn
MBS+Agency Debt, $bn
Total Securities in SOMA, % of GDP, RHS
Source: Federal Reserve, Barclays Research
13
Labor market slack has declined significantly
Labor market slack is steadily falling. Broader measures of unemployment may fall to 2004 levels by late-2015
6
5
4
3
2
1
0
Broad Slack Measure to
decline 0.5% by Q3-15
-1
-2
-3
Jun-92
Jun-94
Jun-96
Jun-98
Jun-00
Unemp - NAIRU
Jun-02
Jun-04
Jun-06
Jun-08
Labor Market Indicators (first factor)
Latest
Average Q2-2004
Distance
Pace of Decline, 1yr
Time to H104 Levels, yrs
Date when hit
U3
5.8
5.6
0.2
-1.4
0.1
Dec-14
Unemp
(>27wks)
1.9
1.2
0.6
-0.8
0.8
Sep-15
Jun-10
Jun-12
Jun-14
Jun-16
Q2-04 Levels
U6
11.5
9.6
1.9
-2.2
0.9
Sep-15
Source: Federal Reserve, Barclays Research
14
7
11/28/2014
Broad range of labor market indicators have shown improvement
Nonfarm employees
Temporary help services
employment
1.2
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
Initial Claims
Unemployment Rate, >27 weeks
Job Openings
Hirings
Quits
Underemp. (U6-U3)
NFIB Hiring Plans
Unemployment Rate
Labor market differential
Difficult to fill (NFIB)
Latest
1y ago
Q104
Q4 06
Q4 09
Source: Haver Analytics, Atlanta Fed, Barclays research.
15
Discouragement has only marginally contributed to the decline
in the overall labor force participation rate
Decline in LFPR over last few years, not due to
discouragement
Only a small fraction of the decline in LFPR over the
last decade attributable to those who want a job
Cumulative Decline in LFPR since End of 2000
1.0
0.5
0.0
-0.5
-1.0
-1.5
-2.0
Dec-00
Dec-02
Dec-04
Dec-06
Dec-08
Dec-10
Dec-12
Retirement
Disability
Want a Job
Don't Want - In School
Don't Want - Not in School
Decline in LFPR, %
population, Q407 to Q413
Total
Want a Job
Retired
Disabled or Ill
In School
Others
16-24y
-0.6%
-0.1%
0.0%
-0.1%
-0.5%
0.1%
Wants a Job/Total
11%
25-50y
-0.3%
-0.2%
-0.1%
0.0%
-0.2%
0.1%
53%
51-60y
-0.4%
-0.1%
0.1%
-0.4%
0.0%
0.0%
22%
61y+
-1.9%
-0.1%
-1.4%
-0.3%
0.0%
0.0%
6%
Total
-3.2%
-0.5%
-1.4%
-0.9%
-0.7%
0.2%
14%
Source: Haver Analytics, Philadelphia Fed, Atlanta Fed, Barclays Research
16
8
11/28/2014
“Maximum Employment” Mandate: Wage inflation is key
Wage inflation should accelerate going forward
All measures of wage inflation have risen from the lows
Source: Haver Analytics, Barclays research
17
Inflation has risen from the lows but remains below target
5%
4%
3%
Core inflation has stagnated at current levels
2%
1%
5%
0%
4%
-1%
3%
-2%
-3%
2%
-4%
Jan-01 Jan-03 Jan-05 Jan-07 Jan-09 Jan-11 Jan-13
1%
Core Services
0%
Core Goods
8
-1%
7
-2%
Jan-01 Jan-03 Jan-05 Jan-07 Jan-09 Jan-11 Jan-13
6
Headline PCE
PCEexcluding Food and Energy
5
4
3
Source: Haver Analytics, Barclays research
2
1
0
Mar-83
Mar-89
Mar-95
Core PCEServices
Mar-01
ECI
Mar-07
Mar-13
AHE
18
9
11/28/2014
Goods inflation to become more of a drag in the near term
Trade weighted USD has strengthened from the lows
Housing inflation has risen from the lows
and should accelerate further
5%
140
12.0
130
4%
11.0
120
10.0
110
3%
2%
100
9.0
1%
90
8.0
0%
-1%
Mar-01
80
May-94
May-99
May-04
May-09
May-14
7.0
Mar-04
Mar-07
Housing Inflation, %, lhs
Mar-10
Nominal Broad Trade-Weighted Exchange Value of the US$ (Jan 97=100)
Mar-13
Rental Vacancy Rates, %, rhs
Pickup in core Goods inflation to be reversed
Non-housing service inflation lower than pre-crisis levels
Recent drop in healthcare due to Sequester
20%
5.0%
10%
15%
5%
10%
4.0%
5%
0%
3.0%
0%
Medicare
Sequester
2.0%
-5%
-5%
-10%
1.0%
-15%
Dec-05
Dec-07
Dec-09
Dec-11
yoy change in Trade Weighted USD
0.0%
-1.0%
Jan-01 Jan-03 Jan-05 Jan-07 Jan-09 Jan-11 Jan-13
Core Services ex-housing ex- heathcare
-10%
Dec-13
yoy change in Core Import Prices, 3M Lagged, rhs
Source: Haver Analytics, Barclays research
Health Care
19
Weak recovery suggests neutral rate is much lower today
According to an estimate, short run real neutral rate has
declined to around 0%
Well below pre-crisis levels due to slower trend growth
and headwinds
4.0
4.0
3.5
3.0
3.0
2.0
2.5
1.0
2.0
1.5
0.0
1.0
-1.0
0.5
-2.0
0.0
-3.0
-0.5
-1.0
Jun-92
Jun-95
Jun-98
Jun-01
Jun-04
Jun-07
Jun-10
-4.0
Jun-92
Jun-13
Estimate of short run real neutral rate
Jun-95
Jun-98
Jun-01
Jun-04
Trend Growth
Jun-07
Jun-10
Jun-13
Headwinds
“The Taylor Rule assumes an equilibrium real rate of interest of 2 percent. This seems much too high in the current economic
environment in which headwinds persist, and somewhat too high even when these headwinds fully dissipate “
– NY Fed Dudley - June’14
Source: Federal Reserve, Barclays Research
20
10
11/28/2014
Fed’s estimate of long term neutral rate may be too high
Fed’s long term fed funds forecast has declined
Trend growth may be lower than the Fed is assuming
4.30
4.5
4.20
4.0
4.10
3.5
3.9
3.2
3.1
3.2
3.0
4.00
2.3
2.5
3.90
2.0
3.80
2.0
1.5
1.5
3.70
1.0
3.60
0.5
3.50
Dec-11
0.5
0.0
Jun-12
Dec-12
Jun-13
Dec-13
Jun-14
1950-1973 1974-1981 1982-1990 1991-2001 2002-2011 2012-2022
Potential Output
Fed's mean estimate of longer run nominal funds rate
Potential Labor Force
Potential Labor Productivity
Median
Source: Federal Reserve, Barclays Research
21
Key Takeaways
Monetary policy has been extremely accommodative post-crisis
Labor market slack has fallen significantly from post-crisis highs
Wage inflation has risen from the lows but remains muted
Inflation has risen from the lows, but downside risks have emerged
Neutral Rates are significantly lower than pre-crisis averages
Patience in unwinding monetary policy accommodation is warranted.
A pickup in Inflation could cause the Fed to tighten at a faster pace
22
11
11/28/2014
What does it mean for the markets?
23
Yields are lower than can be justified by the economic outlook
Benign hiking cycle given the econ outlook
Market is trading well through even this benign cycle
4.0
3.5
3.0
-10
2.5
-17
2.0
-25
-30
1.5
-32
-38
1.0
-44
-50
0.5
2s
0.0
Dec-14
3s
5s
7s
-42
10s
30s
Term Premium, bp
Dec-16
Dec-18
Base Case
Dec-20
Dec-22
Market OIS Rates, %
Dec-24
Source: Barclays Research
24
12
11/28/2014
Low term premium leads to low/negative excess returns
Low term premium is associated with
subsequent low excess returns
Term premium is negative in the intermediate sector
3.0
14
2.5
12
2.0
10
8
1.5
6
1.0
4
2
0.5
0
0.0
-2
-4
-0.5
-6
-1.0
Jun-03
Tsy Index Excess Returns, Subsequent 1 year
-8
-1.00
Jun-04
Jun-05
Jun-06
Jun-07
Jun-08
5y term premium, %
Jun-09
Jun-10
Jun-11
Jun-12
Jun-13
R² = 0.42
-0.50
0.00
0.50
1.00
1.50
2.00
2.50
3.00
Jun-14
10y term premium, %
10y term premium, %
Source: Federal Reserve, Barclays Research
25
Net Supply of Treasuries has been steadily falling
Net
Coupon Foreign
Issuance Sector
Federal
Reserve
Pension
Funds
/Ins
Mutual
funds
Banking
Sector
Others
2010
1,602
781
245
82
131
117
246
2011
1,319
418
642
80
85
-40
134
2012
1,036
576
21
82
110
56
191
2013
799
407
543
49
55
-27
-229
2014E
603
341
252
44
57
128
-220
2015E
463
300
0
40
50
100
-27
Source: Haver Analytics, Barclays Research
26
13
11/28/2014
Demand from foreign investors remains high
Foreign Investors have been buying further
out the curve at Treasury auctions
FX Reserve balances stand at $12trillion. Even modest
growth will result in significant demand for US FI
4.5
10%
40%
Sep-12
Sep-13
Level of FX Reserves, $bn, lhs
Currency adjusted, 12M growth of key countries, %, rhs
2013
2014 YTD
Sep-11
3.0
-5%
Sep-14
2012
7,000
Sep-10
3.5
0%
2005
0%
4.0
20%
2011
5%
8,000
5.0
60%
10,000
9,000
5.5
2010
11,000
80%
2009
15%
6.0
2008
12,000
100%
2007
20%
2006
$bn
13,000
% of purchase in 2y/ 3y/ 5y
% of purchase in 7y/ 10y
% of purchase in 30y
Duration Est (yrs), RHS
Source: Treasury, Bloomberg, Barclays Research
27
Real yields in the US are high in a global context !
10y real yields in the US are low in a historical context but high relative to Europe and Japan
2.0
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5
Jun-10
Dec-10
Jun-11
Dec-11
Jun-12
Dec-12
10y real rates, USD
Jun-13
EUR
Dec-13
Jun-14
JPY
Source: Barclays Research
28
14
11/28/2014
Domestic bank demand has accelerated given new regulation
Mutual fund demand has picked up but not to the levels
prior to the last year selloff
Bank demand for securities has picked up
400
400
300
300
200
200
100
100
0
-100
0
-200
-100
-300
-400
Mar-12 Jun-12
-200
Jan-04
Jan-06
Jan-08
Jan-10
Jan-12
Jan-14
Sep-12 Dec-12 Mar-13 Jun-13
Sep-13 Dec-13 Mar-14 Jun-14
Taxable Bond funds, $bn, 13 weeks, annualized
Net increase in security holdings, 6m annualized
Equity Inflows,$bn, 13 weeks, annualized
Source: Haver Analytics, Barclays Research
29
What could be the big surprise next year? Higher Inflation
Pickup in inflation could trigger faster normalization
Market’s are very sanguine about the inflation outlook
3.5
6
%
5
4
3
3.0
UKBE(RPI):2.73
2
1
2.5
0
2.0
-1
USBE 1.92
-2
-3
1.5
-4
France BE: 1.18
10
11
UKBE10y
12
Eur BE 10y
13
14
France BE 10y
-5
Mar-87
Germany BE:
1.13
1.0
Mar-91
Real FF Rates, %
Mar-95
Mar-99
Mar-03
Real Neutral Rate
Mar-07
Mar-11
Deviation from Neutral, %
USBE 10y
Source: Treasury, Bloomberg, Barclays Research
30
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Key Takeaways: Inflation is key for higher yields
Yields in the US look too low even given the moderate economic outlook
Term Premium is unusually low
Negative Term Premium is associated with low/negative subsequent returns
Global Supply-Demand Factors argue for term premia to unwind gradually
A pickup in Wage/Price Inflation is key for yields to rise
Higher inflation should accelerate the process of policy normalization
Higher inflation should also lead to higher term premia
31
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28/11/2014
SECURITIES DIVISION
Index Annuity Team
ESG
Changing Distribution Paradigm For Fixed
Indexed Annuities and Variable Annuities
Title
10th Annual Equity Based Insurance Guarantees Conference
(Chicago
18 November 2014 (1115 – 1200 hours)
Subtitle
Date
Prepared by a Goldman Sachs sales and trading desk, which may have a position in the
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onlybefor
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Prepared
by GS business
trading desks,
which may
long/short
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mentioned.
otherwise
be subject
to, ideas
CFTC
Regulations
1.71
and Not
23.605.
Prior
communication
of any
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to others
is likely.
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4
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5
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Executive Summary
Recent Developments
 Opportunity set is substantial and leveraged to several factors:
 Continued growth in demand for retirement products
 Reduced competition from variable annuities
 Greater breadth of choices for the consumer
 Banks and broker/dealers are increasingly important distribution channels
 Broader availability of living benefits has expanded the addressable market
 Index selection strategy is a critical success factor for carriers
7
Overview of FIA Market
Fixed Indexed Annuities Annual Sales
Annual Sales (billions)
$50
$44.86
$40
$36.37
$30
$27.43
$24.99
$29.15
$29.77
2010
2011
$32.01
$20
$10
$0
2008
2009
Source: Advantage Compendium & Beacon Research as of Nov 2014
2012
Distribution Trends
Q2 2013
0%
3%
0%
0% 4%
Captive Agents
Captive Agents
14%
Independent Producers
Independent Producers
Wirehouse
Wirehouse
15%
Large/Regional BDs
Independent BDs
80%
Bank & SLs
Direct/Third Party
Source: Beacon Research, as of Nov 2014
2014 Estimated
Q2 2014
0% 5%
12%
2013
Past performance not indicative of future results
1%
1%
Large/Regional BDs
Independent BDs
65%
Bank & SLs
Direct/Third Party
Past performance not indicative of future results
8
4
28/11/2014
Characteristics of Bank/Broker Dealer Distribution
 Key attractions
 Scalability
 More developed suitability standards
 Access to larger and well organized sales forces
 Distribution cost efficiencies
 Considerations
 Strong competition for shelf space
 Lengthy approval processes
 FIA sales are growing within banks they are currently a small proportion of
overall sales
 Product restrictions and requirements
 Product features
 Minimum Carrier Ratings
9
Market Leaders
2014 Q2 FIA Sales – Top 10 FIA Underwriters
Allianz
Security Benefit
Largest sellers of FIA have embraced bespoke indices
American Equity
Great American
Athene USA
Midland National
Voya
EquiTrust Life
Symetra Financial
F & G Life
$M
Source: Beacon Research, as of Nov 2014
$1000 M
$2000 M
$3000 M
$4000 M
$5000 M
$6000 M
$7000 M
Past performance not indicative of future results
10
5
28/11/2014
FIA Growth Profile is Strong Relative to VA
VA Sales Growth vs. FIA Sales Growth
$180
$45
$160
$40
$150
$140
$35
$130
$120
$30
$110
$100
$25
New FIA Sales ($ Billion)
New VA Sales ($ Billion)
$170
$90
$80
VA Sales
FIA Sales
2007
2008
2009
2010
2011
2012
2013
$179
$23
$152
$25
$124
$27
$137
$29
$154
$30
$143
$32
$141
$36
Source: Beacon Research, as of Nov 2014
2014
(Estimated)
$136
$45
$20
Past performance not indicative of future results
11
Living Benefit Adoption Rates are High
Proportion of Policyholders Selecting Living Benefits when Available
76%
74%
74%
72%
71%
71%
71%
70%
69%
68%
67%
66%
64%
62%
2011Q1
2011Q3
Source: LIMRA Secure Retirement Institute , as of Nov 2014
2012Q1
2012Q3
2013Q1
2013Q3
2014Q1
12
6
28/11/2014
The Art of Indexing

Pandora's box has been opened through an unprecedented market environment

Customized indices are now core to most product development in FIA space

New index creation creates opportunities and challenges for annuity providers

Long-term view on product development is key
13
Art of Indexing in Annuity Space
An Overview of Key Considerations
Volatility Control Mechanism
Index Fee
200
300
Uncontrolled Vol
No Drag
5% Vol Target
Hypothetical Returns
Hypothetical Returns
180
160
140
120
100
1% Drag
250
200
150
100
80
50
60
0
1
2
3
4
5
6
7
8
9
0
10
1
2
3
4
5
6
7
8
9
10
Years
Years
Excess Return or Total Return
Diversification and Volatility
220
200
Total Return Index
Concentrated Portfolio
Excess Return Index
175
Diversified Portfolio
Hypothetical Returns
Hypothetical Returns
180
140
100
150
125
100
75
60
50
0
1
2
3
4
5
Years
6
7
8
9
10
0
1
2
3
4
5
6
7
8
9
10
Years
Analysis is for illustrative purposes and is not indicative of future results
14
7
28/11/2014
Benchmark Deviation
170
160
150
140
130
120
110
100
90
80
Jan-11
Jul-11
Jan-12
Jul-12
SPX
Jan-13
Jul-13
Jan-14
Jul-14
SPXTR-ER 5% VC P2P
Source: Goldman Sachs Securities Division, as of Nov 2014
Past performance not indicative of future results
15
Index Selection…No Free Ride
Index Sharpe Ratio
SPX
0.11
SPXTR-ER 5% VC
0.25
220
200
180
160
140
120
100
80
2000 2001
2002
2003
2004
2005
2006
SPX P2P
2007
2008
2008
2009
2010 2011
2012
2013
2014
SPXTR-ER 5% VC P2P
Source: Goldman Sachs Securities Division, as of Nov 2014
Assumptions
Constant 400 bps annual budget
Compare SPX point-2-point performance vs. SPXTR excess return 5% volatility controlled uncapped point-2-point
Past performance not indicative of future results
16
8
28/11/2014
Conclusion

Customized indices in the insurance space are the most significant innovation since
the launch of the VA business.

Regulatory challenges persist as the market continues to evolve

“With great power comes great responsibility”
17
9
11/28/2014
The Last 10 Years Of Retirement Products:
A Consultant’s Viewpoint
MILLIMAN FINANCIAL RISK MANAGEMENT LLC
Ken Mungan, FSA, MAAA
10th Annual Equity Based Insurance Guarantee Conference (Chicago)
18 November 2014 (1330 – 1430 hours)
PROPRIETARY & CONFIDENTIAL
LIMITATIONS AND DISCLOSURES
•
•
•
•
•
•
2
For investment professionals use only. Not for public use or distribution.
Past performance is not indicative of future results. Recipients must make their own
independent decisions regarding any strategies or securities or financial instruments
mentioned herein.
Milliman Financial Risk Management LLC does not make any representations that products or
services described or referenced herein are suitable or appropriate for the recipient. Many of
the products and services described or referenced herein involve significant risks, and the
recipient should not make any decision or enter into any transaction unless the recipient has
fully understood all such risks and has independently determined that such decisions or
transactions are appropriate for the recipient.
Any discussion of risks contained herein with respect to any product or service should not be
considered to be a disclosure of all risks or a complete discussion of the risks involved.
The recipient should not construe any of the material contained herein as investment,
hedging, trading, legal, regulatory, tax, accounting or other advice. The recipient should not
act on any information in this document without consulting its investment, hedging, trading,
legal, regulatory, tax, accounting and other advisors.
Milliman Financial Risk Management LLC does not ensure a profit or guarantee against loss.
PROPRIETARY & CONFIDENTIAL
1
11/28/2014
Agenda
• VA Market Landscape and Product Trends
• Risk Management Technique Updates
• The Managed Risk Fund Landscape
• Managed Risk Funds At work
• Impact of risk management on reserves and capital
3
PROPRIETARY & CONFIDENTIAL
ACLI Presentation 2004
Risk Management Challenges
• Insurers need to manage economic risks & balance
– Regulatory, analyst, & accounting
– Product marketplace requirements
• We now live in a high volatility world
– Insurers exposed to much greater risk
– Regulatory & accounting environment changing drastically
– Policyholders demand protection
4
PROPRIETARY & CONFIDENTIAL
2
11/28/2014
Yields at Historically Low Levels
Corporate Bond Yields
50% A, 50% BBB Public
Market Conditions on December 26, 2003
Gross Yield
(BEY)
2.53%
4.05%
4.66%
5.10%
Maturity
2
5
7
10
5
PROPRIETARY & CONFIDENTIAL
Yield Summary
Corporate Bond Yields: 50% A / 50% BBB Public
9.0%
8.0%
7.0%
6.0%
5.0%
2 Yr
6
5 Yr
7 Yr
Jan-03
Sep-02
May-02
Jan-02
Sep-01
May-01
Jan-01
Sep-00
May-00
Jan-00
Sep-99
Jan-99
May-99
Sep-98
May-98
Jan-98
4.0%
3.0%
2.0%
1.0%
0.0%
10 Yr
PROPRIETARY & CONFIDENTIAL
3
11/28/2014
Equity Risk
Index Returns
Period
2003
2002
2001
2000
1999
7
S&P 500
Russell 2000
27%
47%
-22%
-20%
-12%
3%
-9%
-3%
21%
21%
NASDAQ
49%
-31%
-21%
-39%
86%
Bond
5%
10%
9%
12%
-1%
PROPRIETARY & CONFIDENTIAL
Regulatory Environment
• Statutory regulation has not kept pace with changing
capital markets
• Insurers adopt strategies to “arbitrage”
discrepancies
• Fundamental economic risks will prevail
– Regulation will evolve
– Exposed to regulatory change
8
PROPRIETARY & CONFIDENTIAL
4
11/28/2014
Accounting Framework
• Competing objectives
– Management of economic risks
– Stable GAAP income
• Objectives are converging
– Example: GMDB reserves, DAC process & hedging
program
– Technology & data management challenge
9
PROPRIETARY & CONFIDENTIAL
Variable Annuities
• Implications of market downturn
– Existing business less profitable than expected
– Guarantee benefits exposure
– Awareness of risks
• Rating agencies, regulators, analysts
• Significant opportunity for increased sales &
asset growth!
10
PROPRIETARY & CONFIDENTIAL
5
11/28/2014
VA Policyholder Research
Policyholder Behavior Analysis
Asset Allocations by Issue Year
Issue
Year
2003
2002
2001
1995 - 2000
Stock
42%
44%
56%
63%
Money Mkt &
Bond
Fixed Account
22%
36%
18%
38%
20%
24%
13%
25%
Volatility
8.9%
9.2%
11.4%
13.1%
Market downturn has changed behavior.
11
PROPRIETARY & CONFIDENTIAL
Demographic Factors
Policyholder Behavior Analysis
Asset Allocations by Age Group
Issue
Age
0 to 49
50 to 59
60 to 69
70 to 79
80+
Volatility
12.6%
11.4%
9.8%
8.5%
8.0%
● Women select less aggressive allocations than men
(Volatility 0.5% lower).
● No anti-selection by benefit type.
12
PROPRIETARY & CONFIDENTIAL
6
11/28/2014
Behavior Analysis
• Market down-turn has raised policyholder
awareness of risks
• Older customers favor less aggressive strategies
• Market experience & demographic trends
create strong demand for insurance products
• Opportunities:
– VA guarantees
– Asset allocation assistance
– Flexible pay-down / withdrawal products
13
PROPRIETARY & CONFIDENTIAL
VA Guarantee Hedging
Comparison of Results
Accumulated Net G/L after 10 Years -- with and without Dynamic Hedging
$10 Billion of Initial Premium
Mix of MAV, Rollup, ROP, and EEB
20 Scenarios
200,000
150,000
Accumulated Net G/L after 10 Yrs ($000's)
100,000
50,000
0%
(50,000)
20%
40%
60%
80%
100%
120%
140%
Gross Cumulative 10 Yr Return
(100,000)
(150,000)
(200,000)
Accumulated Net G/L at 10 yrs with Dynamic Hedging
(250,000)
Accumulated Net G/L at 10 yrs without Dynamic Hedging
(300,000)
14
PROPRIETARY & CONFIDENTIAL
7
11/28/2014
Leadership is Critical!
• Variable product management
– Risk / return analysis is complex
• GAAP: DAC, SOP, Hedging
• Statutory: New RBC Rules
• Market position: Sales vs. Safety
– Investment in technology for a competitive edge
– Balance competing objectives
– CEO & CFO involvement is critical!
15
PROPRIETARY & CONFIDENTIAL
Moody’s Insurance Conference 2009
• VA writers’ stock prices declined dramatically during the
financial crisis
• Milliman Research: Develop a comprehensive model to
estimate the market sensitivity of an illustrative $1 billion VA
business
• Typical GMWB design
• Model operational & distribution expenses
• Fixed & variable expense structure
• Assume commissions funded via debt-financing
• Evaluate various levels of guarantee & base revenue hedging
• Model all aspects on a risk-neutral basis
16
PROPRIETARY & CONFIDENTIAL
8
11/28/2014
50,000,000
40,000,000
30,000,000
20,000,000
10,000,000
-20%
-10%
0%
10%
20%
(10,000,000)
(20,000,000)
EPV Profit No Hedging
EPV Profit: 100% Gtee Hedged
EPV Profit: 100% Gtee Hedged, 100% Base Product Revenue Hedged
Full Guarantee Hedging Implies a Beta of 1.74, Assuming 2% Capital
17
PROPRIETARY & CONFIDENTIAL
Experience = 202%
18
PROPRIETARY & CONFIDENTIAL
9
11/28/2014
19
PROPRIETARY & CONFIDENTIAL
Initial Conclusions
• VA business is highly market sensitive
• Market valuation reflects a high degree of market sensitivity
– Conservative view on guarantee hedging
– Potential costs of default events
• More benefit can be derived from
– Redesigning benefits & product structure to reduce market sensitivity
& stabilize margins
• Less benefit from simply increasing capital
20
PROPRIETARY & CONFIDENTIAL
10
11/28/2014
Existing VA Business Model
Funds
Stocks
Insurer Balance Sheet
Guarantee Fee
Bonds
21
■Guarantee Liability
■Hedge Assets
■P&L Volatility
PROPRIETARY & CONFIDENTIAL
Sustainable VA Business Model
Funds
Insurer Balance Sheet
■Reduced & Stabilized
Guarantee Liability
Stocks
Bonds
&
Hedges
Reduced
Guarantee Fee
■Reduced P&L
Volatility
■ Transfer of Hedge
Breakage & Basis
Risk to Policyholder
■ Reduced Behavior
Risk
22
PROPRIETARY & CONFIDENTIAL
11
11/28/2014
VA Market Landscape and Product
Trends
PROPRIETARY & CONFIDENTIAL
Significant moves by key
players
24
PROPRIETARY & CONFIDENTIAL
12
11/28/2014
Trends in VA Market
•
•
•
•
•
•
Demand for guarantees remains strong in spite of higher prices and less generous
features, and living benefit rider take-up rates are in excess of 90% compared with
pre-crisis levels of 50-70%.
GMWBs continue to be the most widespread VA feature and in 2014, policies with
a GMWB rider represented 71% of sales.
Lifetime income riders which guarantee minimum annual income of 4-6% of
account values for life have also been popular.
Many insurers including MetLife and Lincoln have begun mandating allocations to
low-volatility sub-accounts that have embedded hedging.
Insurers have also mitigated policyholder behavior risk by mandating automatic
sub-account rebalancing and/or restricting investment options.
Insurers are incentivizing customer surrenders and annuitization to accelerate the
run-off of low-return blocks.
25
PROPRIETARY & CONFIDENTIAL
Driving the Pullback
Profitability
26
• Competitive market place
• Low interest rate environment
• Customer value proposition
Earnings Volatility
• US GAAP earnings volatility is high versus other lines of
business
Capital Intensity
• Capital requirements potentially increasing
• Potential new capital requirements for certain companies (SIFI)
• Potentially higher ROC on other products / regions
Managing Legacy Inforce
• Products are long-term, ranging from 5-40 years
• Older products had less restrictions
Policyholder Behavior
Assumptions
• Experience is beginning to emerge around policyholder behavior
• Lapses have tended to be lower than assumed
• Withdrawal utilization has shown to be less than optimal
PROPRIETARY & CONFIDENTIAL
13
11/28/2014
In-force Management
Increase Fees
• Many companies have increased fees on existing riders
Fund Restrictions and
Replacement
• Removal of most aggressive asset allocation models
• Substitution of actively managed funds with index funds
• Substitution of static allocation models with risk-managed funds
Rider Buyback Programs
• Optional programs to entice policyholders to cancel rider with a
benefit
• Offered by Transamerica, AXA, Hartford, Voya
Transactions / Reinsurance
27
• CIGNA reinsured in-force to Berkshire Hathaway
• Lincoln entered into a reinsurance agreement with Wells Fargo
PROPRIETARY & CONFIDENTIAL
Product Trends
Reduce Benefits / Increase
Fees
28
• Continued reduction in richness of benefits
• Increased fees
Risk-managed Funds
• Biggest trend in new product design
• Majority of top VA writers mandate use of risk-managed funds for
us with guarantees
• Second generation offer a variety of risk-managed funds
Investment Only Variable
Annuities (IOVA)
• Value proposition is a wide variety of fund choices
• Jackson National has made this popular
• Several companies have increased their targeted sales of Variable
Annuities with no guarantees
Deferred Income Annuity
(DIA)
• Use of DIA to address longevity risk for policyholders
• Previously some companies sold as longevity insurance
• Decouples equity and longevity risk
PROPRIETARY & CONFIDENTIAL
14
11/28/2014
Milliman Hedge Cost Index
•
•
•
Source: Milliman Hedge Cost Index
29
The Milliman Hedge Cost Index™
(MHCI) provides the estimated
hedging cost for a hypothetical
lifetime guaranteed minimum
withdrawal benefit (“Lifetime
GMWB”) block
The MHCI is a widely used reference
point for insurance companies,
investment banks, hedge funds, and
others that participate in the VA
marketplace as issuers or investors.
The expected hedge cost for a
hypothetical lifetime GMWB block
was 82 bps as of September 2014,
up 22 bps from the recent low as of
Dec 2013.
PROPRIETARY & CONFIDENTIAL
THE MANAGED RISK FUND
LANDSCAPE
PROPRIETARY & CONFIDENTIAL
15
11/28/2014
An Overview of Managed Risk Funds
Total VA Managed Risk Funds have about $200 billion of AUM as of 3/31/2014
0.6%
0.2%
5.0%
In descending distribution
order
Asset Allocation
Volatility Management and Related Strategies
Risk Parity
Asset Allocation
25.5%
68.8%
Risk Parity
Asset Allocation/Downside
Asset Allocation/Downside Protection
Protection
Equity Equity
Minimum
Vol Volatility
Minimum
Volatility Management
Milliman Managed Risk
31
PROPRIETARY & CONFIDENTIAL
MANAGED RISK FUND STRATEGIES
32
•
Managed Risk Strategy – A futures based strategy that has both the volatility management
and capital protection components
•
Volatility Management – A derivatives based strategy that seeks to meet a preset volatility
target
•
Asset Allocation – Dynamically allocate asset exposures based on momentum signals to
reduce overall portfolio volatility
•
Asset Allocation/Downside Protection - Dynamically allocate asset exposures based on
momentum signals to reduce overall portfolio volatility and purchase put options
•
Risk Parity - Allocate across equities, fixed income and commodities, such that each asset class
contributes an equal share of risk
•
Equity Minimum Volatility – Allocate assets to stocks with historically low volatility
PROPRIETARY & CONFIDENTIAL
16
11/28/2014
Considerations for VA Writers when
Implementing Managed Risk Funds
Basis Risk
• Frequent rebalancing makes regression based approaches
ineffective
• Requires daily allocations
Hedging Residual
Risk
• Residual risk remains and may be hedged
• Requires detailed modeling of strategy to determine residual
risk
GAAP Impact
• Target volatility allows move away from implied volatility
Reserve and Capital
Credit
• Strategy needs to be transparent enough to model and
provide justification to get reserve and capital credit
33
PROPRIETARY & CONFIDENTIAL
RISK MANAGED FUNDS – BEYOND VA MARKET
Risk Managed Mutual Funds
AUM = 150.1 Billion
7%
33%
26%
Tactical Risk Management
Long/Short Equity
Multialternative
Derivative Hedged Equity
33%
Source: Wall Street Journal Article (June 2nd, 2014 - Alternative Mutual Funds) and analysis of Morningstar data
34
PROPRIETARY & CONFIDENTIAL
17
11/28/2014
HEDGE VEHICLE COMPARISONS
• Implementing a managed risk fund involves choosing how the hedging
operation will be implemented
Risk Management
Strategy
35
Advantages
Disadvantages
Futures
•
•
•
•
•
Options
• Convexity pickup for intraday large
movements
• No interference with security selection
process
• Up-front costs
• Volatility risk premium
• Limited availability, particularly for longer term
and far out-of-the money options
• Potential basis mismatch
Asset Transfer
• Match hedge target exactly
• Cleanly hedge fixed income exposure
•
•
•
•
Low implementation cost
Ability for intra-day trading
High liquidity
Vast market depth
No interference with security selection
process
•
•
•
No convexity pickup
Potential basis mismatch
Futures contracts may not exist for certain
instruments
Inability to trade more than once per day
Disrupts security selection process
Potentially large swings in underlying weights
Higher transaction costs
PROPRIETARY & CONFIDENTIAL
MANAGED RISK FUND UNIVERSE
• The appropriate peer group includes funds that have an active risk
management strategy (e.g. targeting a specific risk level, using a
derivatives overlay to limit downside)
• This does not include funds that look at risk management as part of their
standard asset allocation decisions
• The Peer Group Comparison Charts plot each fund based on its risk/return
profile
• Well performing funds should
• Hit their risk management target
• Preserve a reasonable amount of market return
36
PROPRIETARY & CONFIDENTIAL
18
11/28/2014
MANAGED RISK VIT UNIVERSE – 2013 RESULTS
50%
40%
S&P 500
30%
Developed
Return
20%
10%
0%
Barclays
Emerging
-10%
-20%
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
14.0%
16.0%
Volatility
37
PROPRIETARY & CONFIDENTIAL
MANAGED RISK VIT UNIVERSE – 2014 YTD
RESULTS
15%
Emerging
S&P 500
10%
5%
Return
Barclays
Developed
0%
-5%
-10%
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
14.0%
16.0%
18.0%
Volatility
38
PROPRIETARY & CONFIDENTIAL
19
11/28/2014
MANAGED RISK FUNDS AT WORK
PROPRIETARY & CONFIDENTIAL
THE MANAGED RISK STRATEGY
• The Managed Risk Strategy includes two sophisticated risk management
techniques:
• Volatility Management
• Capital Protection Strategy
• The Managed Risk Strategy reduces downside exposure and increases the
likelihood that investors can achieve funding objectives
40
PROPRIETARY & CONFIDENTIAL
20
11/28/2014
VOLATILITY MANAGEMENT
• Current Asset Allocation
• Targets a specific equity allocation (i.e. 60%) as a proxy for risk
• Maintains constant equity allocation regardless of market conditions
• Target Volatility Asset Allocation
• Targets a specific volatility level directly via a futures overlay
• Seeks to prevent portfolio volatility from dramatically increasing during crises
41
PROPRIETARY & CONFIDENTIAL
CAPITAL PROTECTION STRATEGY
• Capital Protection Strategy seeks to reduce losses in adverse market
environments
• The investor’s portfolio is mapped to major market indices
• Use simple, liquid exchange-traded hedge instruments to replicate a 5-year
rolling maturity put option
• This provides a cushion against losses during major market declines
42
PROPRIETARY & CONFIDENTIAL
21
11/28/2014
USE OF DERIVATIVES
• Both components of the Managed Risk Strategy are implemented with
exchange-traded futures contracts
• Futures are liquid, transparent and cash settle every day
• Alternatives (i.e. put options) have historically included a significant “riskmargin” which will tend to gradually deplete fund values over time
• Volatility management ensures that futures can be used to replicate a put
option with a high degree of reliability
43
PROPRIETARY & CONFIDENTIAL
VOLATILITY MANAGEMENT AT WORK
• Our research shows that volatility models continue to forecast future short
term volatility accurately
• The plot below shows that a strategy using a volatility model to target a
10% volatility tends to do so within narrow band.
45.0%
40.0%
35.0%
30.0%
25.0%
20.0%
15.0%
10.0%
5.0%
0.0%
Q1
'09
Q2
'09
Q3 Q4 - Q1
'09 09 '10
Q2
'10
Q3
'10
Q4
'10
Q1
'11
90% Confidence Interval
Q2
'11
Q3
'11
Q4
'11
Q1
'12
Q2
'12
Static Allocation
Q3
'12
Q4
'12
Q1
'13
Q2
'13
Q3
'13
Q4
'13
Volatility
Model
Milliman
Volatility
Model
Source: Milliman Financial Risk Management LLC.
The performance shown is historical, for informational purposes only, and does not guarantee future results. The performance figures do not reflect charges specific to an individual’s contract, such as
cost of insurance, mortality and expense risks charges, riders and sales charges, which would negatively affect performance. Shares of the Portfolio are sold only through variable policies and are not
available to the general public. Investment return and principal value of an investment will fluctuate so that an investor’s shares, when redeemed, may be worth more or less than their original cost.
Current performance may be lower or higher than the performance data quoted. The backtesting rates of return are hypothetical historical illustrations and do not represent the returns of any particular
investment portfolio.
44
PROPRIETARY & CONFIDENTIAL
22
11/28/2014
CAPITAL PROTECTION STRATEGY
Reduce Losses in Adverse Environments
• Map the current portfolio to major market indices
• Use exchange-traded equity futures contracts on those indices to replicate
a rolling maturity put option on the portfolio
• The result: a cushion against losses is created during major market declines
Behavior of Cushion (created by cash payoffs from futures contracts)
If the market
rises, some
cash is lost,
but the losses
quickly
plateau.
The more the
market falls,
the more
rapidly the
cash cushion
grows.
45
THESE RESULTS ARE BASED ON SIMULATED OR HYPOTHETICAL PERFORMANCE RESULTS THAT HAVE CERTAIN INHERENT LIMITATIONS. UNLIKE THE RESULTS
SHOWN IN AN ACTUAL PERFORMANCE RECORD, THESE RESULTS DO NOT REPRESENT ACTUAL TRADING. ALSO, BECAUSE THESE TRADES HAVE NOT ACTUALLY
BEEN EXECUTED, THESE RESULTS MAY HAVE UNDER-OR OVER-COMPENSATED FOR THE IMPACT, IF ANY, OF CERTAIN MARKET FACTORS, SUCH AS LACK OF
LIQUIDITY. SIMULATED OR HYPOTHETICAL TRADING PROGRAMS IN GENERAL ARE ALSO SUBJECT TO THE FACT THAT THEY ARE DESIGNED WITH THE BENEFIT OF
HINDSIGHT. NO REPRESENTATION IS BEING MADE THAT ANY ACCOUNT WILL OR IS LIKELY TO ACHIEVE PROFITS OR LOSSES SIMILAR TO THESE BEING SHOWN.
PROPRIETARY & CONFIDENTIAL
THE Managed Risk Strategy – LIVE PORTFOLIOS
• Fund performance has demonstrated the benefits of the Managed Risk
Strategy in reducing downside exposure while allowing upside
participation
Source: Milliman Financial Risk Management LLC, 6/9/11 – 8/31/14.
The performance shown is historical, for informational purposes only, not reflective of any investment, and does not guarantee future results. The performance figures do not reflect charges specific to
an individual’s contract, such as cost of insurance, mortality and expense risks charges, riders and sales charges, which would negatively affect performance. Shares of the Portfolio are sold only
through variable policies and are not available to the general public. Investment return and principal value of an investment will fluctuate so that an investor’s shares, when redeemed, may be worth more
or less than their original cost. Current performance may be lower or higher than the performance data quoted.
46
PROPRIETARY & CONFIDENTIAL
23
11/28/2014
Impact on Reserves & Capital
PROPRIETARY & CONFIDENTIAL
Evolution of Risk Management Strategies
48
PROPRIETARY & CONFIDENTIAL
24
11/28/2014
HYPOTHETICAL LIFETIME GMWB – ASSUMPTIONS
VA Product Details
•Lifetime guaranteed minimum withdrawal benefit (GMWB) with tiered
withdrawals, 5% at Age 65
•Starting AV of $1,000,000, Rider charge of 1%, M&E of 2%
•Industry representative mortality, lapse, and utilization assumptions
•Underlying fund is a generic 70/30 equity/bond fund.
•Volatility management target is 16%
•Same product and cells as used to create Milliman Guarantee Index Hedge
Costs
49
PROPRIETARY & CONFIDENTIAL
HYPOTHETICAL LIFETIME GMWB – ASSUMPTIONS
Conditional Tail Expectation (CTE) Details
•AAA Scenarios with daily bridging for protected fund
•PV Worst Accumulated Surplus for living benefit rider only
•Assumes newly issued policies with starting account value adjusted for historical fund
performance at each quarter end
•Three sets of results:
• Real World (RW) Traditional Fund / CTE Traditional Fund – Historical performance based on
non-managed risk returns, forward looking CTE scenarios based on non-managed risk returns.
• RW Managed Risk / CTE Traditional Fund – Historical performance based on managed risk
returns, forward looking CTE scenarios based on non-managed risk returns. Represents
outcome if no credit for managed risk strategy is assumed in forward looking CTE scenarios.
• RW Managed Risk / CTE Managed Risk – Historical performance based on managed risk
returns, forward looking CTE scenarios based on projected managed risk returns. Represents
outcome if reserve/capital benefit from managed risk strategy credit is assumed in forward
looking CTE scenarios.
50
PROPRIETARY & CONFIDENTIAL
25
11/28/2014
HYPOTHETICAL LIFETIME GMWB
HISTORICAL RETURNS
The underlying portfolio consists of 70% S&P 500 and 30% Barclays Aggregated with an 80bps annual fee for the underlying portfolio
and a 100bps annual fee for the risk managed portfolio. The 20bps difference represents the cost of the risk management strategy.
THESE RESULTS ARE BASED ON SIMULATED OR HYPOTHETICAL PERFORMANCE RESULTS THAT HAVE CERTAIN INHERENT LIMITATIONS. UNLIKE THE RESULTS
SHOWN IN AN ACTUAL PERFORMANCE RECORD, THESE RESULTS DO NOT REPRESENT ACTUAL TRADING. ALSO, BECAUSE THESE TRADES HAVE NOT ACTUALLY
BEEN EXECUTED, THESE RESULTS MAY HAVE UNDER-OR OVER-COMPENSATED FOR THE IMPACT, IF ANY, OF CERTAIN MARKET FACTORS, SUCH AS LACK OF
LIQUIDITY. SIMULATED OR HYPOTHETICAL TRADING PROGRAMS IN GENERAL ARE ALSO SUBJECT TO THE FACT THAT THEY ARE DESIGNED WITH THE BENEFIT OF
HINDSIGHT. NO REPRESENTATION IS BEING MADE THAT ANY ACCOUNT WILL OR IS LIKELY TO ACHIEVE PROFITS OR LOSSES SIMILAR TO THESE BEING SHOWN.
51
PROPRIETARY & CONFIDENTIAL
HYPOTHETICAL LIFETIME GMWB
US STATUTORY RESERVE – CTE 70
Risk managed funds provide reduced volatility of capital even without credit for protection in reserve scenarios
Metric
Average
Standard Deviation
RW Traditional/ CTE70 Traditional
14,242
33,029
RW Risk Managed/ CTE70 Traditional
6,861
10,377
RW Risk Managed/ CTE70 Risk Managed
2,904
8,607
The underlying portfolio consists of 70% S&P 500 and 30% Barclays Aggregated with an 80bps annual fee for the underlying portfolio and a 100bps annual
fee for the risk managed portfolio. The 20bps difference represents the cost of the risk management strategy.
52
THESE RESULTS ARE BASED ON SIMULATED OR HYPOTHETICAL PERFORMANCE RESULTS THAT HAVE CERTAIN INHERENT LIMITATIONS. UNLIKE THE RESULTS SHOWN IN AN
ACTUAL PERFORMANCE RECORD, THESE RESULTS DO NOT REPRESENT ACTUAL TRADING. ALSO, BECAUSE THESE TRADES HAVE NOT ACTUALLY BEEN EXECUTED, THESE RESULTS
MAY HAVE UNDER-OR OVER-COMPENSATED FOR THE IMPACT, IF ANY, OF CERTAIN MARKET FACTORS, SUCH AS LACK OF LIQUIDITY. SIMULATED OR HYPOTHETICAL TRADING
PROGRAMS IN GENERAL ARE ALSO SUBJECT TO THE FACT THAT THEY ARE DESIGNED WITH THE BENEFIT OF HINDSIGHT. NO REPRESENTATION IS BEING MADE THAT ANY
ACCOUNT WILL OR IS LIKELY TO ACHIEVE PROFITS OR LOSSES SIMILAR TO THESE BEING SHOWN.
PROPRIETARY & CONFIDENTIAL
26
11/28/2014
HYPOTHETICAL LIFETIME GMWB
US STATUTORY CAPITAL – CTE 90
Risk managed funds provide reduced volatility of capital even without credit for protection in reserve scenarios
Metric
Average
Standard Deviation
RW Traditional/ CTE90 Traditional
36,534
65,241
RW Risk Managed/ CTE90 Traditional
20,543
29,946
RW Risk Managed/ CTE90 Risk Managed
8,552
24,405
The underlying portfolio consists of 70% S&P 500 and 30% Barclays Aggregated with an 80bps annual fee for the underlying portfolio and a 100bps
annual fee for the risk managed portfolio. The 20bps difference represents the cost of the risk management strategy.
53
THESE RESULTS ARE BASED ON SIMULATED OR HYPOTHETICAL PERFORMANCE RESULTS THAT HAVE CERTAIN INHERENT LIMITATIONS. UNLIKE THE RESULTS
SHOWN IN AN ACTUAL PERFORMANCE RECORD, THESE RESULTS DO NOT REPRESENT ACTUAL TRADING. ALSO, BECAUSE THESE TRADES HAVE NOT ACTUALLY
BEEN EXECUTED, THESE RESULTS MAY HAVE UNDER-OR OVER-COMPENSATED FOR THE IMPACT, IF ANY, OF CERTAIN MARKET FACTORS, SUCH AS LACK OF
LIQUIDITY. SIMULATED OR HYPOTHETICAL TRADING PROGRAMS IN GENERAL ARE ALSO SUBJECT TO THE FACT THAT THEY ARE DESIGNED WITH THE BENEFIT OF
HINDSIGHT. NO REPRESENTATION IS BEING MADE THAT ANY ACCOUNT WILL OR IS LIKELY TO ACHIEVE PROFITS OR LOSSES SIMILAR TO THESE BEING SHOWN.
PROPRIETARY & CONFIDENTIAL
HYPOTHETICAL LIFETIME GMWB
US STATUTORY CAPITAL – Standard Scenario
Risk managed funds provide reduced volatility of capital even without credit for protection in reserve scenarios
Amount
The underlying portfolio consists of 70% S&P 500 and 30% Barclays Aggregated with an 80bps annual fee for the underlying
portfolio and a 100bps annual fee for the risk managed portfolio. The 20bps difference represents the cost of the risk management
strategy.
54
THESE RESULTS ARE BASED ON SIMULATED OR HYPOTHETICAL PERFORMANCE RESULTS THAT HAVE CERTAIN INHERENT LIMITATIONS. UNLIKE THE RESULTS
SHOWN IN AN ACTUAL PERFORMANCE RECORD, THESE RESULTS DO NOT REPRESENT ACTUAL TRADING. ALSO, BECAUSE THESE TRADES HAVE NOT ACTUALLY
BEEN EXECUTED, THESE RESULTS MAY HAVE UNDER-OR OVER-COMPENSATED FOR THE IMPACT, IF ANY, OF CERTAIN MARKET FACTORS, SUCH AS LACK OF
LIQUIDITY. SIMULATED OR HYPOTHETICAL TRADING PROGRAMS IN GENERAL ARE ALSO SUBJECT TO THE FACT THAT THEY ARE DESIGNED WITH THE BENEFIT OF
HINDSIGHT. NO REPRESENTATION IS BEING MADE THAT ANY ACCOUNT WILL OR IS LIKELY TO ACHIEVE PROFITS OR LOSSES SIMILAR TO THESE BEING SHOWN.
PROPRIETARY & CONFIDENTIAL
27
11/28/2014
VA Writers’ Risk Position Has Improved
• Risk managed funds significantly reduce the risks faced by VA writers
• Improved stability of capital & reserves
• Preventing portfolio volatility from dramatically increasing during crises
• Providing a cushion against losses during major market declines
• Align competing hedging objectives
• A more stable AV is beneficial to any hedging and accounting
objective
• M&E income is stabilized, leading to smoother DAC amortization
55
PROPRIETARY & CONFIDENTIAL
Milliman, Inc.
Financial Risk Management LLC
71 S. Wacker Dr. - 31st Floor
Chicago, IL 60606
Ph: +1 312 726. 0677
Fx: +1 312 499 5700
www.milliman.com
PROPRIETARY & CONFIDENTIAL
28
VA Hedging
Lessons Learned
Andrew Rallis,
EVP & Global Chief Actuary,
MetLife
10th Annual Equity Based Insurance Guarantee Conference (Chicago)
18 November 2014 (1330 – 1430 hours)
1
Top Ten List of Things that
Matter (2004)
1. Accounting
2. Software
3. Hardware
4. Communication
5. Accountability
6. Delta
7. Interest rates
8. Volatility
9. Behavior
10. Risk profile
2
1
Top Ten List of Things that
Really Matter (2014)
1. Liquidity
2. Scale
3. Capital
4. Transparency
5. Accountability
6. Cross-Greeks
7. Interest rates
8. Volatility Control
9. Behavior
10. Risk Appetite
3
Why Accounting Matters
•
•
•
•
Economic vs GAAP vs Stat
Economic gain or loss
GAAP income volatility
Statutory solvency
Small product variations can drive big
changes in strategy
4
2
Why Software Matters
•
•
•
•
•
Production Environment
Speed of Execution
Connectivity
Support
Consistency of Hedge and Reserve
calculations
• Stochastic-in-stochastic
5
Why Hardware Matters
•
•
•
•
Test vs Production Environment
Distributed Processing
Speed of Execution
Security
6
3
Why Communication Matters
• Internal Constituents
–
–
–
–
–
Boards of Directors
Executive Group
Risk Management
Treasury and Valuation
Auditing
• External Constituents
– Regulators
– Rating Agencies
7
Why Accountability Matters:
Roles & Responsibilities I
Function
Responsible Unit
Project Management
Program Scope
Program Implementation
Program Monitoring
ALM Unit
ALM Unit
ALM Unit
Risk Analysis / Product Considerations
Product / Business Specifications
Hedge Level Determination
New Funds
Product Development
ALM Unit
Product Management &
Marketing
8
4
Roles & Responsibilities II
Function
Responsible Unit
Project Structure
State Filings of Reinsurance Treaties
Monitor Impact on Captive
Reinsurance Unit
Reinsurance Unit
Accounting Aspects
Overall Accounting Treatment
Operational Accounting Structure
Transactional Accounting
Accounting Oversight
Margin Account Maintenance
Accounting Technical Services
Controllers - Controls (SOX &
Technical Accounting)
Controllers - Controls
Reporting & Specialized Acct
Derivative Accounting
9
Roles & Responsibilities III
Function
Responsible Unit
Financial Management & Reporting
GAAP & STAT Financials
Reserves - Reporting
DAC Calculations
SOP Reserves
Tax Issues
Business Finance
Valuation Unit
Actuarial Services
Actuarial Services
Tax Department
Corporate Oversight
Program Oversight
Enterprise Risk Management Impact
Auditing
Corporate Risk Management
Corporate Risk Management
Internal Auditing
10
5
Roles & Responsibilities IV
Function
Investment Aspects
Trading Investments
VA Portfolio Management
Responsible Unit
Derivatives Unit
Portfolio Management Unit
Cash & Capital Management
Cash Management
Capital Management
Board Resolution
Corporate Treasury
Corporate Treasury
Legal
Information Technology (IT) Aspects
IT Project Manager
Administrative Systems
Software (MGHedge) Maintenance
IT
IT
Milliman (Consultants)
11
Why Delta Matters
• Delta = Sensitivity of Economic Value to
equity movements
• SOP liabilities less sensitive to equity
movements
• Balance hedging of Economic Value to
GAAP income volatility
12
6
Why Interest Rates Matter
• Rho
– Sensitivity of Economic Value to interest
rate movements
– Discounting of claims and fees
– Bond fund performance
– Level of claims (GMIB in-the-moneyness)
• Cross Greeks
– Impact on dynamic delta hedging
13
Why Volatility Matters
• Vega = Sensitivity of Economic Value to
equity volatility movements
• SOP liabilities not as sensitive to these
movements
– SOP 03-1 Paragraph 31
– implied vol versus historic vol
• Balance hedging of Economic Value to
GAAP income volatility
14
7
Key Rate Vega
12.0%
10.0%
8.0%
Vega,
% Total
6.0%
4.0%
2.0%
1.30
1
0.0%
Maturity Bucket, years
19
16
13
10
7
4
1.00 ITM
0.70
15
Why Behavior Matters
•
•
•
•
Little experience
Uncertainty in calibrating parameters
Un-hedgeable
We use a binomial model for assessing
residual behavior risk
• Typically 1-2% of account value
16
8
Why Risk Profile Matters
• Diversification offsets
• Managing total company interest rate
risk
• C3 Phase II -> C1 Equity component for
covariance offset
– impact various by legal entity
• Internal economic capital formulas
17
Why Liquidity Really Matters
In a crisis , difficult to move assets
• Counterparties
• Systemic Risk?
• Dodd-Frank
18
9
Why Scale Really Matters
• Sophisticated Hardware and Software
– Nested Stochastics
– Cloud Computing
• Key Personnel
• Model Calibration
19
Why Transparency Really
Matters
• Stakeholders’ sophistication
• Continuity
• Complexity
20
10
Why Accountability Really
Matters
• Attract & Retain Key Staff
• Decision Making
21
Why Cross Greeks Really
Matter
Delta vs. parallel shift in Int Rates
Delta: sensitivity to 1% shift in all equity indices simultaneously
14
Total GMIB Delta
Euro Put
12
10
8
6
4
2
-150
-100
-50
0
50
100
150
Yield Curve Shifts
22
11
Why Interest Rates Really
Matter
• Managed Interest Rate Environment
• Does Mean Reversion Exist ?
• How Risky Are Bond Funds ?
23
Why Volatility Control Matters
• Limited supply of vol
• Accounting asymmetry
• Principle protection
24
12
Why Behavior Really Matters
•
•
•
•
Un-hedgeable
Emerging Experience
Valuations are highly leveraged
Predictive Modeling
25
Why Risk Appetite Really
Matters
• How much is too much?
• Governance
26
13
VA Hedging
Lessons Learned
27
14