Download Investment Strategies and Alternative Investments in Insurance and

Session 172 PD, Investment Strategies and Alternative Investments in
Insurance and Pension Portfolios
Moderator:
Thomas J. Egan Jr., FSA, EA, FCA
Presenters:
Kathleen Patrice Brolly, FSA
Ming Chiu, FSA, MAAA
Kelly Lynn Featherstone, FSA, ACIA, CFA
ALTERNATIVE
INVESTMENTS
SOA 2014 Annual Meeting & Exhibit
Session 172 PD: Investment Strategies and
Alternative Investments in Insurance and Pension
Portfolios
Kelly Featherstone
October 29, 2014
AGENDA
1. Who is AIMCo?
2. What are Alternative Investments?
3. Why Invest in Alternatives?
4. Risks of Investing in Alternatives
5. Inclusion of Alternative Assets in Pension Plan
Portfolios
6. Regulatory Change and Investment Opportunities
2
WHO IS AIMCO?
• A high performing investment manager that finds the best
opportunities from around the world, and delivers results.
One of Canada’s largest and most diversified institutional
investment managers with more than $84 billion of assets
under management
• A crown corporation responsible for the investments of 27
pension, endowment and government funds in Alberta and
work closely with our clients to ensure our strategies meet
their objectives.
• On behalf of our clients, AIMCo places investments across
all asset classes and markets to maximize return on risk.
• We manage equity, fixed income, private equity,
infrastructure, timberlands, mortgages, real estate, absolute
return and venture investments in house. We also have a
allocation to externally managed assets.
3
WHAT ARE ALTERNATIVE
INVESTMENTS?
• Anything that is not stocks, bonds or cash
• e.g. real estate, private equity, infrastructure, timber,
commodities, mortgages, hedge funds, structured
products, etc.
• Each alternative is unique but alternative investments
may exhibit the following characteristics:
– Relative illiquidity
– Complexity
– Higher transaction and/or management costs
– Low correlations with traditional assets
– Non-normal or nonlinear return profiles
– Infrequent valuations and/or difficult to value
– Difficult to benchmark
4
SOME ALTERNATIVE INVESTMENTS
EXAMPLE: HEDGE FUNDS
• Liquidity: moderate (typically monthly, quarterly, annually,
or longer lockups).
• Return profile: typically target consistent absolute returns
• Risks: leverage, market risks, illiquidity
• Correlations with Stocks and Bonds: varies by strategy,
low but positive for CTA strategies, higher for long short
or directional equities
• Complexity: varies by strategy, lack of transparency can
be an issue
• Possible Benchmarks: HFRX global benchmark, strategy
specific benchmark, CS hedge fund index
• Costs: base management fee 1-2% per year, 10-20%
performance fee
5
WHY INVEST IN ALTERNATIVES
• Low correlations with existing portfolio (diversification)
Improve the efficient frontier
• Improve expected portfolio return
• Reduce risk of total portfolio
• Capture illiquidity or other risk premia
• Hedge liabilities
• Tail hedge
The Role of Alternative Investments in a Diversified Investment Portfolio By Baird Private Wealth Management
https://www.rwbaird.com/bolimages/Media/PDF/Whitepapers/Demystifying-the-Role.pdf
6
WHY INVEST IN ALTERNATIVES: LOW
CORRELATIONS (DIVERSIFICATION)
Correlation Coefficient 19982013
Goldman Sachs Commodity Index
NCREIF - Timberland Index
NCREIF - National Index
HFRX Global Hedge Fund Index
HFRX Macro-CTA Index
HFRX EH- Equity Market Neutral Index
S&P 500 (Total Return)
GS
Commodity
Index
1.000
Timber
Real Estate
HFRX Global
HFRX Macro- HFRX Equity S&P 500 (TR)
CTA
Market Neutral
Barclays
Aggregate
Bond Index
0.018
0.089
0.426
0.243
0.097
0.273
0.347
-0.001
1.000
0.490
0.079
0.164
0.058
0.033
0.074
0.031
1.000
0.121
0.135
0.157
0.013
0.024
-0.135
1.000
0.666
0.128
0.549
0.620
0.078
1.000
0.043
0.100
0.173
0.140
1.000
0.020
0.034
-0.047
1.000
0.964
-0.091
1.000
-0.056
MSCI World Free - Net
Barclays Aggregate Bond Index
1.000
The Role of Alternative Investments in a Diversified Investment Portfolio By Baird Private Wealth Management
https://www.rwbaird.com/bolimages/Media/PDF/Whitepapers/Demystifying-the-Role.pdf
7
MSCI World
WHY INVEST IN ALTERNATIVES:
HIGHER RETURN POTENTIAL
Returns in USD
Goldman Sachs Commodity Index
1998
1999
-35.75%
40.92%
NCREIF - Timberland Index
NCREIF National Property Index
2000
2001
2002
2003
2004
49.74% -31.93%
32.07%
20.72%
17.28%
2005
2006
25.55% -15.09%
2007
2008
2009
2010
2011
2012
2013
25.44% -46.49%
13.48%
9.03%
-1.18%
0.08%
-1.22%
-4.76%
-0.16%
1.58%
7.75%
9.68%
-6.46% -16.86%
13.11%
14.26%
10.54%
10.99%
5.88%
10.92%
4.41%
-5.25%
1.88%
7.66%
11.20%
19.35%
13.68%
18.43%
16.24%
11.36%
12.24%
7.29%
6.74%
8.99%
14.48%
20.06%
16.59%
15.84%
9.52%
HFRX Global Hedge Fund Index
12.94%
26.66%
14.29%
8.67%
4.72%
13.39%
2.69%
2.72%
9.26%
4.23% -23.25%
13.40%
5.29%
-8.87%
3.51%
6.72%
HFRX Macro-CTA Index
14.76%
25.82%
12.50%
8.32%
14.04%
14.61%
-0.32%
6.67%
5.61%
3.19%
5.61%
-8.78%
-1.73%
-4.88%
-1.00%
-1.79%
5.27%
3.11%
3.81%
-3.72%
11.83%
2.83%
-2.38%
0.32%
0.21%
4.76%
-1.16%
-5.56%
2.64%
-2.92%
-4.66%
1.72%
S&P 500 TR
HFRX Equity Hedge Market Neutral
28.58%
21.04%
-9.11% -11.88% -22.10%
28.68%
10.88%
4.91%
15.79%
5.49% -37.00%
26.46%
15.06%
2.11%
16.00%
32.39%
MSCI World Free - Net
24.38%
24.82% -13.18% -16.82% -19.89%
33.11%
14.72%
9.49%
20.07%
9.04% -40.71%
29.99%
11.76%
-5.54%
15.83%
26.68%
4.11%
4.34%
2.43%
4.33%
5.93%
6.56%
7.86%
4.23%
-2.02%
Barclays Aggregate Bond Index
8.67%
-0.83%
Returns in USD
11.63%
8.42%
10.27%
6.96%
5.24%
Annualized 1998-2013
Goldman Sachs Commodity Index
6.42%
NCREIF - Timberland Index
6.99%
NCREIF National Property Index
9.71%
HFRX Global Hedge Fund Index
6.02%
HFRX Macro-CTA Index
5.79%
HFRX Equity Hedge Market Neutral
1.04%
S&P 500 TR
8.15%
MSCI World Free - Net
7.87%
Barclays Aggregate Bond Index
5.58%
The Role of Alternative Investments in a Diversified Investment Portfolio By Baird Private Wealth Management
https://www.rwbaird.com/bolimages/Media/PDF/Whitepapers/Demystifying-the-Role.pdf
8
RISKS OF INVESTING IN ALTERNATIVES
• Liquidity risk
• Negative skew and/or fat tailed return distributions
• Complexity and operational risks
• Interest rate risk
• Commodity price risk
• Inflation risk
• Credit risk
• Asset class specific risks
9
INCLUSION OF ALTERNATIVE ASSETS
IN INVESTOR’S PORTFOLIOS - CANADA
• Alternatives were once the domain of the very wealthy & well connected.
• The low correlations and high returns earned by these pioneers drew in other
more cautious investors like pension funds who have increased their
allocations over time
Data from http://www.piacweb.org/publications/index.html
10
ALTERNATIVE ASSETS IN INVESTOR’S
PORTFOLIOS – US, UK & AUSTRALIA
• Uptake of Alternatives has differed over time and by geography – likely
influenced by both regulatory and cultural factors but the general trends are quite
similar across institutional pension portfolios
11
ALTERNATIVE ASSETS IN INVESTOR’S
PORTFOLIOS – GLOBAL TRENDS
P7 includes Australia, Canada, Japan, Netherlands, Switzerland, the UK and the US
12
REGULATORY CHANGES AND
OPPORTUNITIES IN THE ALTERNATIVE
SPACE
• Bank prop desks being closed & spun out (Volker
Rule)
• Regulatory capital relief trades
• Sale of bank owned asset due to banking regulatory
change
Pension Plans plans have a long time horizon, unlike
most other institutional investors. This long time
horizon can serve to provide stability to financial
markets as well as earn excess returns for the
pension plans who use this advantage
13
Ming Chiu, FSA, MAAA
Strategic Asset Allocation for Global Multiline Insurer’s Portfolio
Regulatory and Rating Agency Capital Charges for
Alternative Investments
•
Statutory RBC Rules
•
Rating Agency Capital Rules
S&P Capital Rules: For U.S. life companies, hedge funds and private equities are Schedule BA common stock assets. S&P Capital
Charge is 38%. S&P currently uses a consolidated GAAP model for P&C companies. Private equity is exposed to the country specific
charge of 38% (U.S. equity) and an incremental charge of 13%
•
•
2
A.M.Best’s BCAR model:
For U.S. P&C companies, BCAR is a consolidated statutory model
Source: S&P Criteria Insurance General, June 7,2010
(1) Correlation is high-level marginal estimate at the portfolio level
(2) The range for BCAR reflects the 160% AM Best Minimum and best estimate
A Static Hedge Fund Replication Model
•
Based on a paper by Hasanhodzic J. and Andy Lo, we used a multi-variate linear model to replicate hedge fund returns from
1996 to 2010
•
We used 6 tradable liquid derivative indices to represent market risk exposures in FX (US Dollar Index), Rates (JPM US Corp
AA Index), Credit (CDX NA IG), Equity (SP 500), Commodity (GSCI Total Return Index), Volatility (VIX Index).
Factors
Tradable Derivative or Index
USD
US Dollar Index
BOND
JP Morgan US Corporate Investment Grade Index (JGAGUSUS)
CREDIT
CDX NA IG (Data starts from November 2005)
S&P
S&P 500 Total Return
COMMODITY
Goldman Sachs Commodity Index Total Return
DVIX
VIX Index
Rt    1USD   2 BOND   3CREDIT   4 S & P   5COMMODITY   6 DVIX   t
E[ Rt ]    1 E[USD ]   2 E[ BOND ]   3 E[CREDIT ]   4 E[ S & P ]   5 E[COMMODITY ]   6 E[ DVIX ]
Var[ Rt ]  1 Var[USD]   2 Var[ BOND]  3 Var[CREDIT]   4 Var[S & P]  5 Var[COMMODITY]  6 Var[ DVIX ]  Co var iance  Var[ t ]
2
2
2
2
2
2
* Hasanhodzic, J. and Lo. A. (2007). "Can Hedge Fund Returns be Replicated?: The Linear Case." Journal of Investment
Management, 5, 5-45.
Calibration of Hedge Fund Replication Model
•
It can be seen that most of the monthly returns come from alpha. The model can replicate the monthly return for hedge funds with lower
volatility. However, this is based on in-sample regression. Out of sample tests have not been performed
•
The market exposure is concentrated on equity (SP500) and Commodity (GSCI Index). It shows that hedge fund returns are still being
influenced by broad economic cycles as demonstrated by equity and commodity returns
Market Regime Analysis for the Hedge Fund
Replication Model
•
The replication model uses CDX NA IG quotes from Bloomberg directly, thus not being adjusted to reflect carry and DV01 weighting. Also,
CDX NA IG data is only available since 2006. For the following analysis, Credit variable is removed from the model.
•
Separate 1996 to 2010 into 4 market regimes: 1996-1999, 2000-2003, 2004-2007, 2008-2010. Run the multi-variate regression on 5
variables, we get the following table for model coefficients, R squares, and t-stat for each coefficients.
•
The betas are not stable in different market regimes. For example, beta for GSCI Index goes from 0.228 in 1996-1999 period to 0.029 in
2000-2003 period, then back to 0.226 in 2008-2010 period. The alpha in distressed market regime is significantly lower than normal
market environment. Alpha is 0.10% in 2008-2010 period versus 0.90% in overall 1996-2010 period.
•
The model performs better in distressed regime 2008-2010 with 3 out of 5 variables having t-stat > +/-2 and R square of 76%. The
general model in 1996-2010 has SP500 and GSCI Index variables with t-stat > 2 and 40% R square.
•
The FX (Dollar Index) and Rates (JPM Corp AA Index) have unstable betas as they flip between positive and negative in different regimes.
Progression of Asset Portfolio Optimization
Mean-Variance Analysis
 History of Model Development:
Roy (1952), Markowitz (1952):
Efficient Frontier;
Markowitz (1956): Properties and
formulas for efficient frontier;
Tobin (1958): Risk free assets;
Sharpe (1964), Lintner (1965):
borrow and lend at risk free rate;
 Measure of Risk:
Variance, Stadard deviation;
Semideviation (downside
deviation);
Mean Absolute Deviation;
VaR;
CVaR;
Probability of failing;
6
Black-Litterman Model
 Mean-Variance Analysis Results
Un-stable:
Asset weightings are extremely
sensitive to a prior expected return,
volatility, and covaraince
assumptions
 Initial Equilibrium Assumption:
Asset allocation should be
proportional to the market value of
the available assets
 Portfolio Managers’ View:
PM’s are required to state the
assumptions about expected returns
differ from the market and the
degrees of confidence in the
alternative assumptions
 Bayesian Approach to MeanVariance Efficient Optimization
with Constraints:
Risk Factor Based SAA
 Asset Side Risk Factors
 Easily to implement investor’s view to
the risk factor directly
 Provides a base for comparison across
factor from different asset classes -direct attribution of performance to the
underlined risk factors
 Diversification through risk factors –
mimic portfolios
 Liability Side Risk Factors
Model Property Casualty payout patterns to
macro econnomic variables and hidden
processes called Structural Drivers
Risk Factor Based SAA Model
Investment
& Risk Driver
Choice
 Investment Universe:
Govt bonds, Corp bonds,
Equity, Structured
Products, etc.
 Representative Security:
Index, Insurer holding
portfolio
 Risk Drivers: Interest
rates, credit spreads,
equity returns, FX rates,
etc.
Risk Factor
Scenario
Generation
 Full valuation based on
scenarios of future risk
factors
 Historical data and model
based baseline scenario
 Alternative scenario
based on PM’s view
 Return distributions for both
baseline/alternative
scenarios
 Correlation across risk
factors is based on
historical data and
assumed to be stable
Posterior
Risk
Analytics
7
Asset Valuation
& Return
Distribution
 Returns are derived
variables for fixed income
instruments instead of
directly modeled variables








NII
OTTI
SMR
LACR
LAR
VaR
Duration
Unrealized P/L
projection
Portfolio
Optimization
 Mean-variance optimization
with investment constraints
to maximize the total return
of the surplus portfolio
 Optimization separately for
baseline/alternative return
distribution
 Integrated PM’s view with
baseline via return
distributions – BlackLitterman /Robust meanvariance
Investment Universe and Risk Drivers
Asset Class
JPY
Government
Corporate IG Bonds
Common Stock
Common Stock REITs
HFA RMBS
GBP
Government
Corporate IG Bonds
Common Stock
UK RMBS
EURO
Government (ex/GIIPS)
Corporate IG Bonds
Corporate HY Bonds
Common Stock
CDO-CLOs
USD
Corporate IG Bonds
Corporate HY Bonds
EM Government
EM Corporate
Common Stock
Hedge Funds
CDO-CLOs
Non-agency RBMS
ABS
CMBS
CML
Bank Loans
8
Investment Universe
Barclays JP Agg Treasury + Gov. Rel
Barclays JP Agg Corp.
TOPIX
TSE REIT
New Issue from JP Govt.
Barclays Sterling Agg Treasury
Barclays Sterling Agg Corp
FTSE100
JPM Euro RMBS AAA
Barclays EU Agg Treasury &Gov
Barclays EU Agg Corp
Barclays Pan-Euro HY
STOXX
JPM New Issue AAA
Barclays US Agg Corp
Barclays US Corp HY
JPM EMBI (IG)
JPM CEMBI (IG)
S&P 500
HFRX global index
JPM CLO 2.0
Insurer Holding
Barclays ABS index
Barclays AAA CMBS
Barclays BBB CMBS
CSFB Loan Index
Total Return Fixed Income Historical Data
Fixed
Fixed
X
X
Amortizing
Fixed
Fixed
X
Floater
Fixed
Fixed
Fixed
X
Floater
Fixed
Fixed
Fixed
Fixed
X
X
Floater
X
Fixed
Fixed
Fixed
Floater
FX Rates
RiskMetrics T-curve
RiskMetrics Spread
TOPIX total return
TSE REIT total return
JHF Launch Spreads
FX Rates
RiskMetrics T-curve
RiskMetrics Spread
FTSE 100 total return
JPM Euro RMBS AAA Spread
FX Rates
RiskMetrics T-curve
RiskMetrics Spread
RiskMetrics Spread
STOXX 50 total return
JPM New Issue AAA Spread
FX Rates
RiskMetrics Spread
RiskMetrics Spread
EM Gov Spread
EM Corp Spread
S&P 500 total return
HFRX global index
JPM CLO 1.0 New Issue Spread
Insurer total return
Barclays ABS index
Barclays AAA CMBS
Barclays BBB CMBS
CSFB Loan Index
Model
B&H
B&H
B&H
B&H
Internal
Internal
B&H
B&H
B&H
B&H
Internal
B&H
B&H
B&H
B&H
B&H
Internal
B&H
B&H
B&H
Internal
Internal
B&H
Internal
Internal
Internal
Internal
Internal
Internal
Internal
Modeling for Risk Drivers
Historical Risk Factor Data
 Monthly Historical data
from 1/2008 to present
 Marginal distribution and
correlation matrix are
computed for all risk
factors
 All treasury
interest rates,
corporates credit
spreads, equity
returns and FX
scenarios
 Credit rating
transition and
default scenarios
9
 A linear projection model
Economic Scenario Generation
Simulation with
Scenario
Augment
Model
 All structured product asset
classes
 Scenarios are calibrated by
marginal distribution based
on historical data for
baseline scenario
 Scenario-means are
calibrated to match PM’s
view based on PM survey
Future Valuation of Assets and Return
Distributions
Asset Class
Fictitious Bond
Modeled initial ratings only
include AA, A, BBB, and BB
CCY
JPY
Rating
AA
Coupon
2.18%
Maturity
2.11yr
Price
104.18
Time 0 Curves
Base Treasury Rates
Credit Spread
Valuated
Basis = 19bps
Scenario 1 at Time 1
Rating
AAA
AA
A
BBB
BB
B
CCC
Default
Migration Prob.
38%
62%
0%
0%
0%
0%
0%
0%
Prob. Weighted
Return 1
10
Scenario 2 at Time 1
Rating
AAA
AA
A
BBB
BB
B
CCC
Default
Migration Prob.
0%
32%
41%
5%
6%
7%
2%
8%
Prob. Weighted
Return 2
Scenario 999 at Time 1
Rating
AAA
AA
A
BBB
BB
B
CCC
Default
Migration Prob.
5%
94%
1%
0%
0%
0%
0%
0%
Prob. Weighted
Return 999
Scenario 1000 at Time 1
Rating
AAA
AA
A
BBB
BB
B
CCC
Default
Migration Prob.
0%
79%
19%
1%
1%
0%
0%
0%
Prob. Weighted
Return 1000
Portfolio Optimization
•
Optimization Target: Economic Surplus = Assets - Liabilities
 Maximize total return of economic surplus;
 Minimize volatility of economic surplus
•
Each asset taxonomy is modeled as a proxy with representative bonds/cash flows
•
Linear and Quadratic Constraints:
 Duration / Solvency (SMR) / NII / Liquidity / Turnover / Foreign investments
•
Optimization done on baseline and with PM’s view separately
•
Combine optimization results using Black-Litterman method
Society of Actuaries Annual Meeting
Investment Strategies and Alternative Investments in Insurance and Pension Portfolios Kathleen P. Brolly, F.S.A.
Senior Vice President
Senior Institutional Consulting Strategist
Retirement & Benefit Plan Services
Boston, MA
(617) 434‐7857
[email protected]
Bank of America Merrill Lynch does not provide actuarial services or legal or tax advice.
The importance of taking an asset / liability perspective
Asset‐only perspective
• Long time horizon of pension plan suggested ability to take on investment risk to reduce long‐term costs
• Companies assessed investment performance relative to long‐term return expectations
Illustrative Pension Plan Funded Position
100
10% return favorable to
8% assumption
50
0
Asset / liability perspective
• New emphasis on the net funded status
-50
• Market value of liability requires use of current market interest rates and behaves most like a long‐
duration bond
-100
• How assets change relative to liabilities affects net funded status from one period to the next
25% increase due to
decrease in interest
rates and other factors
Asset
Liability
Funded
Status
Year 1
50
-60
-10
Year 2
55
-75
-20
Net impact is 100%
decrease in funded
status
For Society of Actuaries 2014 Annual Meeting use only. Distribution to any other audience is prohibited.
For institutional use only. Distribution to any other audience is prohibited.
1
Asset-liability modeling process
1. Focus on
policies/actions
that can be controlled
2. Test each action under
a very wide range of
scenarios
3. Evaluate metrics
that are critical to
organization
Portfolio Return
For Society of Actuaries 2014 Annual Meeting use only. Distribution to any other audience is prohibited.
For institutional use only. Distribution to any other audience is prohibited.
2
Discovery questions
Funding Policy
Accounting Policy
Benefits Policy
 Is there a specific funding policy or
 Tolerance for changes in the Expected
 Any possible plan close, plan freeze, or
funding target in place for the plan
beyond paying minimum required
contributions?
 Would additional discretionary
contributions be considered?
Return on Assets (ERoA) assumption
that may result in better downside
protection but increase income statement
expense?
plan termination on the horizon?
 If applicable, timing on possible plan
changes?
Key Metrics
Trade-offs
Other
 Is primary objective to minimize absolute
 For example, is the downside protection
 Significant asset allocation to cash?
 Any other restrictions on asset classes?
 Anything else to note about the plan or
value of or volatility of cash contributions?
 Is primary objective to minimize income
statement expense or balance sheet and
income statement expense volatility?
 Is primary objective to achieve/maintain
certain funded status?
of cash contributions in the worst case
scenarios worth foregone asset returns?
If so, to what extent?
your organization’s approach to
retirement plan management?
For Society of Actuaries 2014 Annual Meeting use only. Distribution to any other audience is prohibited.
For institutional use only. Distribution to any other audience is prohibited.
3
Capital market assumptions, current & alternative asset allocations
Asset Classes
Inflation
Cash
U.S. large cap
U.S. mid cap
U.S. small cap
International developed
Emerging market
U.S. investment grade aggregate
Global high yield FI
Hedge Funds
Real estate
Corporate 1-10 year
Corporate 10+ year
Long-Term
Expected Return
Standard
Deviation
2.30%
2.10%
8.30%
8.90%
9.10%
7.50%
13.10%
3.40%
4.10%
6.20%
7.40%
3.80%
5.30%
1.50%
1.50%
17.80%
18.80%
19.50%
18.90%
30.50%
4.30%
14.80%
9.60%
12.70%
5.10%
8.20%
Mix A
Mix B
Current Asset
75/25
60/40
Allocation
BAML BestThinking (partial LDI)
~75/25
(no LDI)
Mix C
50/50
(all LDI)
Mix D
40/60
(all LDI)
Mix E
30/70
(all LDI)
Mix F
20/80
(all LDI)
0.00%
0.00%
45.00%
8.00%
7.00%
8.00%
2.00%
20.00%
2.50%
5.00%
2.50%
0.00%
0.00%
0.00%
0.00%
24.50%
10.00%
6.50%
13.00%
8.50%
22.00%
3.00%
9.50%
3.00%
0.00%
0.00%
0.00%
0.00%
19.00%
8.00%
5.00%
11.00%
7.00%
20.00%
0.00%
7.00%
3.00%
2.00%
18.00%
0.00%
0.00%
16.00%
6.50%
4.00%
9.00%
6.00%
0.00%
0.00%
6.00%
2.50%
6.00%
44.00%
0.00%
0.00%
13.00%
5.00%
3.00%
7.00%
5.00%
0.00%
0.00%
5.00%
2.00%
7.00%
53.00%
0.00%
0.00%
10.00%
4.00%
2.00%
5.00%
4.00%
0.00%
0.00%
4.00%
1.00%
8.00%
62.00%
0.00%
0.00%
6.00%
3.00%
2.00%
3.00%
2.00%
0.00%
0.00%
3.00%
1.00%
9.00%
71.00%
Geometric Mean Return
Standard Deviation
7.57%
13.18%
7.76%
12.86%
7.37%
10.43%
7.33%
9.77%
6.95%
8.71%
6.56%
7.95%
6.07%
7.40%
Equities
Fixed
Alternatives
Cash / Short Term
70.0%
22.5%
7.5%
0.0%
62.5%
25.0%
12.5%
0.0%
50.0%
40.0%
10.0%
0.0%
41.5%
50.0%
8.5%
0.0%
33.0%
60.0%
7.0%
0.0%
25.0%
70.0%
5.0%
0.0%
16.0%
80.0%
4.0%
0.0%
Liability Driven Investments (LDI) to match accounting liability duration of 9 –
when interest rate drops 1%, liability increases 9%
The Defined Benefits Solutions Group , through defined strategic inputs, develops long‐term (multiple market cycle) return assumptions. Future results are not guaranteed. The table shows potential mean and standard deviation of expected return, calculated by the Defined Benefits Solutions Group for select asset classes. Please refer to the Appendix for a more complete explanation of the calculation methodology for these numbers and for disclosures on the different asset classes included in this chart.
For Plan Sponsor and Consultant use only. Distribution to any other audience is prohibited. For institutional use only. Distribution to any other audience is prohibited.
For Society of Actuaries 2014 Annual Meeting use only. Distribution to any other audience is prohibited.
4
Dynamic de-risking strategy
• De-risking is the dynamic and rule based asset allocation strategy by which a pension plan's strategic asset allocation
becomes more conservative as the funded status of the plan improves
⁻ Reduces funded status volatility
⁻ Deploys risk when rewarded to do so; the reward being lower contributions
⁻ Documented, systematic process removes emotional, kneejerk decision-making and supports fiduciary requirement –
tomorrow’s committee understands what is implemented today and why
⁻ Recognizes that no asset allocation can entirely eliminate contributions required during severe underfunding, but creates
a path to target end-state
‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ De‐Risking ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐>
PBO Funded
Status (Market
Value of Assets/
Projected Benefit
Obligation (PBO))
Target Strategic
Asset Allocation
Below 85%
85%
90%
95%
100%
105%+
Mix A
Mix B
Mix C
Mix D
Mix E
Mix F
75/25 no LDI
60/40 partial LDI
50/50 LDI
40/60 LDI
30/70 LDI
20/80 LDI
25%
40%
12%
63%
50%
Potential Expected
Return on Assets
(ERoA)
Assumption
7.75%
7.50%
5%
60%
50%
10%
25%
33%
41%
7%
9%
7.25%
7.00%
70%
6.50%
16%
4%
80%
6.00%
For Plan Sponsor and Consultant use only. Distribution to any other audience is prohibited. For institutional use only. Distribution to any other audience is prohibited.
For Society of Actuaries 2014 Annual Meeting use only. Distribution to any other audience is prohibited.
5
Current & alternative asset allocations –
Funded status at 10/1/2022
ERISA Funded Status
114%
PBO Funded Status
115%
108%
105%
102%
106%
100%
99%
91%
92%
85%
Current
Mix A
Mix B
5th - 25th percentile
Mix C
Mix D
25th - 50th
Mix E
Mix F Dynamic
50th - 75th
75th - 95th
Current
Mean
The numbers represent return projections broken down by percentile for selected asset mixes. The percentiles displayed in the forecast reflect various confidence levels of the statistical model used. For any mix/time period, there is a 50% probability that the results will fall in the range between the 25th and 75th percentiles. There is a 25% probability that they would be greater than the 75th or less than the 25th percentile and a 5% probability that they would be greater than or less than the 95th
or 5th percentiles, respectively. Source: Defined Benefits Solutions Group
Mix A
Mix B
83%
Mix C
80%
Mix D
84%
78%
77%
Mix E
Mix F Dynamic
Current = 77.5 Eq & Alts / 22.5 FI
Mix A = 75 Eq & Alts/ 25 FI
Mix B = 60 Eq & Alts / 20 FI / 20 LDI
Mix C = 50 Eq & AIts / 50 LDI
Mix D = 40 Eq & Alts / 60 LDI
Mix E = 30 Eq & Alts / 70 LDI
Mix F = 20 Eq & Alts / 80 LDI
For Plan Sponsor and Consultant use only. Distribution to any other audience is prohibited.
For institutional use only. Distribution to any other audience is prohibited.
For Society of Actuaries 2014 Annual Meeting use only. Distribution to any other audience is prohibited.
6
Ongoing monitoring & reporting
• Ongoing reporting will include a Quarterly Liability Based Report (sample to the right) to measure the plan’s funded status, and will serve as the basis to determine whether a funded status trigger point has been reached if a de‐risking strategy is adopted
For Society of Actuaries 2014 Annual Meeting use only. Distribution to any other audience is prohibited.
For institutional use only. Distribution to any other audience is prohibited.
7