Download Measuring and Monitoring Financial Systemic Risk

Measuring and Monitoring Financial
Systemic Risk
Presentation to G-20 Conference on “Financial
Systemic Risk” September 27-28 2012
Dale Gray, Sr. Risk Expert, Monetary and
Capital Markets Department, IMF
The views expressed in this presentation are those of the authors and should not be
attributed to the International Monetary Fund, its Executive Board, or its
management.
Outline of Presentation
I.
Systemic Risk Monitoring
II.
Information Sources and a Taxonomy of
Systemic Risk Models
III.
Using Contingent Claims Analysis (CCA) for
Financial, and Sovereign, Systemic Risk Analysis
2
A PRACTICAL APPROACH TO SYSTEMIC RISK MONITORING*
– IMF Monetary and Capital Markets Department
Motivation:
Need to know when to act
No magic bullet (single model) to monitor SR
Many tools/approaches developed over time (~60-70), but only some
suitable for Systemic Risk Monitoring
Increasing need to make the toolkit understandable and usable
A practical user guide (not an academic survey)
– Which best tools and combinations of tools
– For which types of risks?
– In which country categories?
– For measuring which phase of risk—early warning, impact of crisis,
amplification
*Systemic Risk Monitoring Report will be published in October 2012; it was prepared
by IMF MCM staff - Nicolas Blancher, Srobona Mitra, Hanan Morsy, Akira Otani,
Tiago Severo and Laura Valderrama.
21 different tools mapped to
six key questions
2-page templates to describe each
tool:
– main characteristics
– strengths and weaknesses
– uses
– example
Monetary and Capital Markets Department
Toolkit Coverage:
– Types of risks: credit, liquidity, market risks;
– Types of tools: single risk indicators, macrofinancial, network, market-based, hybrid and
structural models;
– Impact of shocks;
– Long-term build up in balance sheet
vulnerabilities;
– Spillovers across financial entities, especially
using high-frequency market price-based tools;
– Cross border contagion between banking systems.
Key Findings: How to Use Tools
A combination of tools should be used
The selection of tools is country-specific
Tools for different phases in systemic risk:
– The slow buildup of risk (e.g., through combinations of
balance-sheet and slow-moving indicators).
– The identification of weak points and potential adverse
shocks (e.g., stress tests to detect weak financial
institutions, asset price deviation from fundamentals).
– The fast unfolding of crisis, including through
amplification mechanisms (e.g., high frequency marketbased spillover measures).
Longstanding data gaps remain an obstacle
Key Findings: Limitations
Early warning. The forward-looking properties of systemic
risk measures are generally weak, even though some
measures appear relatively promising
Thresholds. More work needed on clear and reliable
signals indicating when “to worry” and take action.
System’s behavior. The capacity to model aggregate agent
behaviors is limited, for instance as regards how banks
internalize the materialization or increasing likelihood
of systemic risk; potential reverse feedbacks and multiround effects (i.e., “perfect storms”); and, nonlinear
risk correlations during periods of financial distress.
(See summary of tools: coverage, inputs, outputs,
applicability, bibliography in Appendix to this
presentation)
II. Information Inputs;
Towards a Taxonomy of Risk Models
Risk models use Different Sources of Information:
Accounting balance sheet information
Equity market (levels, returns, option prices)
Bond and CDS spreads
Interbank network linkages
Exposure data
Value-at-Risk
Other
8
Relationships between Risk Models for Individual
Institutions Using Accounting and Market Information
Accounting
Balance
Sheet
Traditional
Balance
Sheet
Indicators
Market Equitybased
Contingent
Claims Analysis
(CCA); MKMV
Equity levels
and returns,
Advanced CCA Equity option
with Govt
implied
contingent
volatility and
liabilities
skew
Distress
CDS/spread
implied PoD
Insurance
Premium
CDS or Bond implied Probability
of Default (PoD)
Market Debt-based
(CDS & bond spreads)
Systemic Risk Models for Multiple Entities
Network
Interbank
Exposures
Accounting
Balance
Sheet
Aggregate
FSIs
Lo –Sherman
model
Merton-based
Network
Market
Equity-based
Merton-type
CCA Joint
Risk
Equity Joint Tail Risk
(from returns- SES
and MES)
Equity option
implied joint risk
Systemic
CCA
CDS/spread
implied
JPoD
CDS CoRisk
Systemic
Distress
Insurance
Premium
CDS –Joint
Probability of
Distress (CoPoD,
BSI)
Market Debt-based
(CDS & bond
spreads)
CoVaR
III. Using Contingent Claims Analysis (CCA) for
Financial, and Sovereign, Systemic Risk Analysis
Core Concept of Contingent Claims Analysis (CCA):
Merton Model
Assets
Equity
or Jr
Claims
Risky
Debt
Assets =
Equity
=
Equity
+
+
• Value of liabilities
derived from value of
assets.
• Liabilities have
different seniority.
• Randomness in
asset value.
Risky Debt
Default-Free Debt – Expected Loss
= Implicit Call Option + Default-Free Debt – Implicit Put Option
11
CCA is Generalization of Black, Scholes, Merton Option
Pricing Theory
Liabilities derive their value from assets;
Asset value is stochastic, changes in future asset
value, relative to the promised payments on debt are
the driver of forward-looking values of equity and
risky debt;
Risky debt is default-free debt value minus the
expected loss value (ELV) due to default;
It helps explain complex risk transmission and
amplifications;
If volatility is set to zero, result is the accounting
balance sheet, default/credit risk measure is lost.
12
CCA Framework has Several Benefits
Tools and techniques for calibrating CCA balance sheets of
corporates and financial institutions are decades old.
Commercial and academic daily models available.
CCA is useful for modeling bank funding cost and can
account for government guarantee impacts on bank
funding cost or spillovers from high sovereign spreads.
Combining individual institution expected losses into
system-wide joint expected losses is useful for analysis
of financial systemic risk, government contingent
liabilities, sovereign risk interactions and frequently
leads GDP.
13
Key CCA Risk Indicators for Financial
Institutions
Expected Default Frequency (EDF, 1 yr in %)
Expected Loss Value (ELV=implicit put option value $)
Expected Loss Ratio (EL, in basis points)
=ELV/Default Barrier=RNDP*LGDsector
Fair Value Spread (in basis points)
FV Spread=-(1/T)*LN(1-EL)
Ratio of Market Capitalization/Assets
(CCA Capital Ratio, in %)
14
Typical Bank – Non-linear Relationships Between CCA
Capital Ratio, EL, EDF and Fair-Value Spreads
Mkt Cap/Assets Ratio (CCACR in %)
0.1
10%
0.08
8%
0.06
6%
y = 0.9854x-0.494
R² = 0.7648
0.04
4%
y = 0.0272x-0.265
R² = 0.8541
88
7
0.02
2%
EDF (%)
66
5
44
3
0 0%
22
1
0
0
0
00
1000
1000
2000
2000
3000
3000
4000
4000
5000
5000
EL Ratio (in bps)
200
200
400
400
600
600
y = 320.9x 0.5
R² = 0.829
800
800
1000
1000
y = 0.1217x1.0815
R² = 0.9987
1200
1200
Fair Value Spread (bps)
Based on Moody’sKMVCreditEdge data
and IMF calculations
Low Risk Zone: CCACR>2.5%, EDF<1.5%,
EL<1800 bps, FVSpread<380 bps
15
CCA provides Estimates of “Market-Implied” Government
Contingent Liability (Citigroup example 2007-2011)
1600
1400
1200
1000
800
FVCDS
600
MARKIT CDS
CCA-based FV
credit spreads
greater than
observed CDS
spreads (in bps)
400
200
0
450
400
350
300
Estimated
“market
implied"
government
contingent
liability (billion
US $)
250
200
150
100
50
0
30-Apr-07
30-Apr-08
30-Apr-09
30-Apr-10
30-Apr-11
16
Systemic CCA: Measuring And Stress Testing
Financial Sector Tail-risk Losses and
Contingent Liabilities
Beginning with CCA models of individual banks,
expected losses and market implied contingent
liabilities are estimated
Multivariate extreme value dependence model is
then used to calculate the multivariate density of:
(i) the banking system expected losses, and,
(ii) government’s contingent liabilities accounting
for dependence
(iii) measures of financial sector tail risk (95% VaR
or Expected Shortfall)
(iv) macro-factor model linked to CCA to stress test
systemic tail risk.
(See Gray and Jobst 2009, 2010, 2011, and
forthcoming)
17
Extreme Tail Risk – US Financial System -Joint Total
Expected Loss Value including dependence structure,
36 largest US financial institutions, 95th percentile
Lehman
Collapse
Total Cont. Liab. (sum of individual expected losses)
Total Cont. Liab. (ES, 95th percentile)
3,000
2,000
1,000
Just after Lehman there was a 5% chance of
$3 trillion losses in financial system over a
one year horizon!
Jan-10
Dec-09
Nov-09
Oct-09
Sep-09
Aug-09
Jul-09
Jun-09
May-09
Apr-09
Mar-09
Feb-09
Jan-09
Dec-08
Nov-08
Oct-08
Sep-08
Aug-08
Jul-08
Jun-08
May-08
Apr-08
Mar-08
Feb-08
Jan-08
Dec-07
Nov-07
Oct-07
Sep-07
Aug-07
Jul-07
Jun-07
May-07
0
Apr-07
In US dollar billions
4,000
18
Extreme Tail Risk – US Government Contingent
Liabilities including dependence structure, 36 largest
financial institutions, 95th percentile
(Contributions of individual institutions can also
be measured.)
19
Joint EL Ratio =
Joint Expected Losses (f
(from
Systemic CCA
CCA)/ Aggregate
Market Capitalization
Systemic CCA Shows Nonlinearities of Asset Volatility,
Market Capital/Assets, Fair Value Credit Spreads, and
Joint Losses – for individual institutions and for the
system as a whole (illustrated below)
Systemic Risk
Assessment
Aggregate MCAR =
Aggregate Market Capitalization/
Aggregate Implied Assets
Implied Asset
Volatility
(Sample Median)
Aggregate Fair Value Credit
Spread
Jobst and Gray (2012)
20
Latest Stage of Crisis: Destabilizing Feedback Loops
between Sovereigns and Banks are Amplifying Risk
Spillovers from the Sovereign to the Banks
and Banks to Sovereigns
A. Mark-to-market fall in
value of govt bonds
held by local banks
DOMESTIC
SOVEREIGN
E. Similar
sovereigns come
under pressure
FOREIGN
B. Increase in bank
funding costs
BANKS
C. Erosion in potential
for official support
D. Mark-to-market fall in
value of govt. bonds
held by foreign banks
G. Rise in counterparty credit risk
SOVEREIGN
I. Increase in contingent
liabilities of govt.
I. Increase in
contingent
liabilities of
govt.
F. Contagion channels
(A, B, & C as above)
BANKS
H. Withdrawal
of funding for
risky banks
21
Measuring Bank, Sovereign, Corporate, Macro, Risk
Spillovers in EU Using CCA-GVAR Framework
(Ongoing joint work with three ECB staff (Marco Gross, Matthias Sydow,
and Joan Paredes); Forthcoming Working Paper
Framework for analysis the interactions of banking
sector risk, sovereign risk, GDP growth, stock
markets, and other macro variable for 15 EU countries
plus the US.
Uses CCA risk indicators (EL) for the banking systems
and corporate sectors and sovereigns in each country,
Together with the GVAR (Global Vector Autoregression)
model for each country, and weight matrices, impulse
responses captures the non-linearity of changes in
bank assets, equity capital, bank credit spreads,
sovereign spreads and corporate credit risk.
22
Inputs and Scenarios/shock origins and Generalized Impulse
Reponses (GIRs) for Banks, Sovereign, Corporates, GDP, etc.)
CCA bank by bank risk
indicators (63)
CCA sovereign credit
risk indicators (16)
CCA corporate credit
risk indicators (16)
CCA banking system
risk indicators (16)
GDP data (16 series)
Stock market index (16)
Other
Scenarios and Shock Origins
GVAR
Model
Copula Simulation
EL (l) and CDS (r) BANKS
EL (l) and CDS (r) SOVEREIGNS
14.0%
140
12.0%
120
10.0%
100
8.0%
80
6.0%
60
4.0%
40
25.0%
8.0%
300
7.0%
250
6.0%
15.0%
200
5.0%
10.0%
150
4.0%
100
5.0%
2.0%
20
0.0%
0
DE IE DK NL FR PT AT ES IT CH UK BE SE NO US GR
EL (l) and CDS (r) CORPORATE
350
20.0%
Weighting Matrices
(16 local
country models)
50
GDP
25
0
ES IT SE NO NL GR FR CH IE AT DK PT BE DE UK US
ES CH DE SE NL US FR AT BE PT NO GR
0.00%
20
15
10
3.0%
-0.10%
-0.20%
-0.30%
-0.40%
2.0%
5
-0.50%
0
-0.60%
1.0%
0.0%
IE UK DK IT
0.0%
ES BE IT PT CH DK SE DE FR US UK NO NL AT GR IE
-0.70%
23
Scenario responses are fed into banking, sovereign,
corporate modules to get secondary outputs
Scenario Responses
Corporate Module
Banking Module
Banking system responses
(ELs and spreads)
Bank by bank output results :
- Funding cost impacts
- Expected Losses
- Implied market capital
impact
- Government
contingent liabilities
Corporate output results :
- Expected Losses
- Credit Spreads
GDP growth IRs
Stock market IRs
Banking system IRs
Corporate sector IRs
Sovereign IRs
Sovereign Module
Sovereign output results :
- Expected Losses
- Credit Spreads
- Other
EU and Euro Zone
Aggregate banking and sovereign
debt expected losses
Aggregate bank capital impact
Fiscal and Growth
Module
-
Contingent liabilities
Borrowing cost changes
Debt to GDP changes
24
Ongoing Work: CCA-GVAR Risk Exposure Outputs Facilitate Analysis
of a Range Risk Mitigation Policy Options to get to ‘Low Risk Zone’
Banks:
Increase bank capital higher assets, lower expected losses
Debt-to-equity conversion lower default barrier, higher equity
Guarantees on bank debt
Ring-fenced asset guarantees
lower bank borrowing costs
Lower asset volatility
Sovereigns:
Increase debt maturity
lower spreads
Fiscal adjustment higher sovereign assets
Supranational:
Sovereign debt purchases/guarantees by public entity (ECB,
EFSF, ESM, other) lower sovereign spreads
Other Monetary Policy OMT/QE purchases lower spreads,
higher GDP growth
25
Thank you
(Contact information: [email protected])
Appendix
Systemic Risk Monitoring Tools and
Bibliography
References for CCA/Systemic Risk
Models
26
Summary of tools for Systemic Risk Monitoring
Thresholds
Early
Warning
Impact of
crisis
Amplification
Y
Y
Y
N
N
Limited
Limited
Y
Limited
Y
Y
Y
Y
Y
Y
Y
Y Y
Y
Y
N
Y
N
Y
Y
Y
Y
Y
Y
N
N
N
Asset prices and
balance sheet data
Limited
Y
Y
Y
N
Y
N
N
BoP and fiscal data
Y
Y
Y
Y Y
Y
N
Y
N
Y
Y
Y
N
Y
Y Y
N
Y
N
Y
Y Y Y Y
N
Y
Y
N
Y
Y
Y
N
Low Income
Y
Type of Data
Y
Frequency
Y
Sectors
Y
CoVaR
Y
N
Financial
High
Asset prices and
balance sheet data
Limited
CoPoD/BSI
Volatility Spillovers
Distress Spillovers
Market-Based
Probability of
Default
Y
Y
Y
N
N
Y
Financial
Financial
Financial
High
High
High
Asset prices
Asset prices
Asset prices
Y
N
Financial and
corporate
High
DSA
N
N
External and
public
Low
Asset Price
N
Y
N
Medium
Balance Sheet
Analysis
N
N
All main
sectors
Low
Systemic CCA
Y
N
Financial
High
Low
Cross-Border
Interconectedness
Y
N
Banking
Additional
Characteristics
Q1
Q2
Q3
Q4
Q5
Q6
Applicability across
Questions
Advanced
Applicability across
Countries
Emerging
Data Requirements
Markets
Coverage
Institutions
Tools
Asset prices and
cash flow data
Sectoral balance
sheet data
Limited Limited
Y
Y Y
Y
Y
Y
Asset prices and
balance sheet data
Limited
Y
Y
Y
Cross-border
banking exposure
and balance sheet
data
Limited
Y
Y
Y
Y
Y
Financial
High
Asset prices and
balance sheet data
N
Limited
Y
Regime switching
N
Y
Financial
high
Asset prices
Y
Y
Y
Y
Low
Cash flow and
balance sheet data
Y
Y
Y
Y
Low
Macroeconomic
data
Y
Y
Y
Y Y
Y
Amplification
Advanced
N
Impact of
crisis
Emerging
Y
Early
Warning
Low Income
Systemic liquidity
Additional
Characteristics
Thresholds
Type of Data
Applicability across
Questions
Frequency
Applicability across
Countries
Sectors
Data Requirements
Markets
Coverage
Institutions
Tools
Q1
Q2
Q3
Q4
Q5
Q6
Summary of tools (cont’d)
Y
N
N
Y
N
Y
Y
Y
Y
N
N
Y
N
N
Y
Y
N
Y
FSIs
Y
Y
Financial,
corporate and
household
Thresholds Model
N
N
Financial
Macro stress tests
Y
N
Financial
Asset prices and
balance sheet data
Limited
Y
Y
Y
Y Y Y
N
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
GDP at Risk
Credit to GDP-Based
Crisis Prediction
Model
Crisis Prediction
Model
DSGE Model
Y Y
Y Y
N
N
N
Low
Asset prices and
macroeconomic
data
N
N
Financial
Low
Macroeconomic
data
Limited
Y
Y
Y Y Y
Y
Y
Y
N
N
N
N
Low
Macroeconomic
data
Limited
Y
Y
Y Y Y
Y
Y
Y
N
N
N
N
Low
Macroeconomic
data
Y
Y
Y
Y
Y
N
N
Y
Y
Financial and
public
Corporate
and
household
Y
Bibliography of Models and Tools for Systemic Risk
Monitoring
Adrian, Tobias and Markus Brunnermeier, 2010, “CoVaR,” Federal Reserve Bank of New York Staff Reports.
Arsov, Ivailo, Elie Canetti, Laura Kodres and Srobona Mitra, forthcoming, “Near-Coincident Indicators of
Systemic Stress”, IMF Working Paper, (Washington: International Monetary Fund).
Baldacci, E., McHugh, J. and Petrova, I., 2011, “Measuring Fiscal Vulnerabilities and Fiscal Stress: A Proposed
Set of Indicators”, IMF WP/11/94, (Washington: International Monetary Fund).
Basel Committee of Banking Supervision (BCBS), 2012, “Models and Tools for Macroprudential Analysis,” May,
available at https://www.bis.org/publ/bcbs_wp21.htm
Benes, Jaromir, Michael Kumhof, and David Vavra, 2010, “Monetary Policy and Financial Stability in EmergingMarket Economies: An Operational Framework,” paper presented at the 2010 Central Bank Macroeconomic
Modeling Workshop, Manila. www.bsp.gov.ph/events/2010/cbmmw/papers.htm.
Borio and Drehmann, 2009, “Assessing the Risk of Banking Crises—Revisited,” BIS Quarterly Review, March.
Chan-Lau, Jorge, Srobona Mitra, and Li Lian Ong, 2012, “Identifying Contagion Risk in the International Banking
System: An Extreme Value Theory Approach," International Journal of Finance and Economics, available at
http://onlinelibrary.wiley.com/doi/10.1002/ijfe.1459/abstract.
Čihák, M., Muñoz, S., and Scuzzarella, R., 2011, “The Bright and the Dark Side of Cross-Border Banking
Linkages,” IMF Working Paper 11/186, (Washington: International Monetary Fund).
De Nicolò and Lucchetta, 2010, “Systemic Risks and the Macroeconomy,” IMF Working Paper 10/29.
Bibliography (cont’d)
Dell’ Ariccia, Giovanni, Deniz Igan, Luc Laeven, Hui Tong, Bas B. Bakker, Jérôme
Vandenbussche, 2012, “Policies for Macrofinancial Stability: How to Deal with the Credit
Booms,” http://www.imf.org/external/pubs/cat/longres.aspx?sk=25935
Diebold, Francis X. and Kamil Yilmaz, 2009, "Measuring Financial Asset Return and Volatility
Spillovers, With Application to Global Equity Markets," Economic Journal, Vol.119, pp.
15–71.
______, 2012, “Better to Give than to Receive: Predictive Directional Measurement of
Volatility Spillovers,” International Journal of Forecasting, Vol. 28, Issue 1, January –
March.
Espinosa-Vega, M., and J. Sole, 2010, “Cross-Border Financial Surveillance: A Network
Perspective,” IMF Working Paper No. 10/105.
González-Hermosillo, Brenda and Heiko Hesse, 2009, “Global Market Conditions and
Systemic Risk,” IMF Working Paper No. 90/230.
Gray, Dale, F. and Andreas A. Jobst, 2011, “Modeling Systemic Financial Sector and
Sovereign Risk,” Sveriges Riksbank Economic Review, 2011:2.
Laeven and Valencia, 2010, “Resolution of Banking Crises: The Good, the Bad, and the Ugly,”
IMF Working Paper 10/146 (Washington: International Monetary Fund).
Lopez-Espinosa, G., Moreno, A., Rubia, A., and Valderrama, L. (2012), “Short-term Wholesale
Funding and Systemic Risk: A Global CoVaR Approach, IMF Working Paper 12/46
(Washington: International Monetary Fund).
Bibliography (cont’d)
International Monetary Fund, 2002, “Information Note on Modifications to the Fund’s Debt
Sustainability Assessment Framework for Market Access Countries,” July.
_____ , 2003, “Sustainability Assessments-Review of Application and Methodological Refinements,”
June.
______, 2006, FSI Compilation Guide (http://www.imf.org/external/pubs/ft/fsi/guide/2006/)
______, 2007, “Republic of Croatia: Selected Issues,” IMF Country Report No. 07/82.
______, 2009, “Assessing the Systemic Implications of Financial Linkages,” Global Financial Stability
Report, April, http://www.imf.org/External/Pubs/FT/GFSR/2009/01/index.htm
______–Financial Stability Board, 2010, “Early Warning Exercise: Design and Methodological Toolkit”,
(Washington: International Monetary Fund).
http://www.imf.org/external/np/pp/eng/2010/090110.pdf
______, 2010, “Recovery, Risk, and Rebalancing,” Chapter 1 in World Economic Outlook, World
Economic and Financial Surveys (Washington, October).
______, 2011a, “Japan: Spillover Report for the 2011 Article IV Consultation and Selected Issues,”
IMF Country Report No. 11/183, July.
______, 2011b, “Towards Operationalizing Macroprudential Policy: When to Act?” Chapter 3, Global
Financial Stability Report, September.
______, 2011c, “Mexico: 2011 Article IV Consultation,” IMF Country Report No. 11/250, July.
______, 2011d, “How to Address the Systemic Part of Liquidity Risk,” Chapter 2, Global Financial
Stability Report, April.
Bibliography (cont’d)
Lund-Jensen, K, 2012, “Monitoring Systemic Risk based on Dynamic Thresholds,” IMF Working Paper
12/149 (Washington: International Monetary Fund).
Moretti, Marina, Stéphanie Stolz, and Mark Swinburne, 2008, “Stress Testing at the IMF,” IMF
Working Paper 08/206, (Washington: International Monetary Fund).
Reinhart, C. M. and K. S. Rogoff, 2010, “From Financial Crash to Debt Crisis,” NBER WorkingPaper No.
15795 (forthcoming American Economic Review).
Reint Gropp & Marco Lo Duca & Jukka Vesala, 2009. "Cross-Border Bank Contagion in
Europe," International Journal of Central Banking, vol. 5(1), pp. 97–139, March.
Segoviano, Miguel and Charles Goodhart, 2009, “Banking Stability Measures,” IMF Working Paper No.
09/04 (Washington: International Monetary Fund).
Schaechter, A, C. Emre Alper, Elif Arbatli, Carlos Caceres, Giovanni Callegari, Marc
Gerard, Jiri Jonas, Tidiane Kinda, Anna Shabunina, and Anke Weber, 2012, “A Toolkit to Assessing
Fiscal Vulnerabilities,” IMF WP/12/11.
Severo, Tiago, 2012, “Using Violations of Arbitrage to Measure Systemic Liquidity Risk and the Cost of
Liquidity Insurance,” IMF Working Paper (Washington: International Monetary Fund)
Sun, Tao, 2011, “Identifying Vulnerabilities in Systemically-Important Financial Institutions in a
Macro-financial Linkages Framework,” IMF WP/11/111,
http://www.imf.org/external/pubs/cat/longres.aspx?sk=24841.
References on Macrofinancial risk and CCA:
Macrofinancial Risk Analysis, Gray and Malone (Wiley Finance book Foreword by Robert Merton)
Gray, D. F., Merton R. C. and Z. Bodie, 2008, “A New Framework for Measuring and Managing
Macrofinancial Risk and Financial Stability,” Harvard Business School Working Paper No. 09-015.
Gray, D., A. Jobst, 2011, “Modeling Systemic Financial Sector and Sovereign Risk,” Sveriges Riksbank
Economic Review, September Gray,
Dale F., Robert C. Merton, and Zvi Bodie. (2006) “A New Framework for Analyzing and Managing
Macrofinancial Risks of and Economy” Harvard Business School Working Paper, No. 07-026, 2006.
(Also NBER Working Paper Series, No. 12637.)
Gray, D. F., Merton R. C. and Z. Bodie, 2007, “Contingent Claims Approach to Measuring and Managing
Sovereign Credit Risk, Journal of Investment Management, Vol. 5, No. 4, pp. 5-28.
Gapen M. T., Gray, D. F., Lim C. H., Xiao Y. 2008, “Measuring and Analyzing Sovereign Risk with
Contingent Claims,” IMF Staff Papers Volume 55 Number 1 (Washington: IMF).
Gray, D. F., Jobst, A. A., and S. Malone, 2010, “Quantifying Systemic Risk and Reconceptualizing the Role
of Finance for Economic Growth,” Journal of Investment Management, Vol. 8, No. 2, pp. 90-110.
Garcia, C., D. Gray, L. Luna, J. Restrepo, 2011, “Incorporating Financial Sector into Monetary Policy
Models: Application to Chile,” IMF WP/11/22
Gray, D. and S. Malone, 2012, “Sovereign and Financial Sector Risk: Measurement and Interactions”
Annual Review of Financial Economics, 4:9.
Gray, D., M. Gross, J. Paredes, M. Sydow, 2012, “Modeling the Joint Dynamics of Banking, Sovereign,
Macro, and Financial Risk using Contingent Claims Analysis (CCA) in a Multi-country Global VAR”
forthcoming
Gray, D. F., and A. A. Jobst, 2012, “Systemic Contingent Claims Analysis (Systemic CCA) – Estimating
Potential Losses and Implicit Government Guarantees to Banks,” IMF Working Paper (Washington:
International Monetary Fund), forthcoming.
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Macrofinancial Risk Analysis
Framework integrates riskadjusted balance sheets
using Contingent Claims
Analysis (CCA) of financial
institutions, corporates,
and sovereigns together
and with macroeconomic
and monetary policy
models
TOOLKIT FOR MACRO RISK
ANALYSIS
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