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Using Contingent Claims Analysis (CCA)
to Measure and Analyze Systemic Risk,
Sovereign and Macro Risk
September 13, 2012
Presentation to Macro Financial Modeling
Conference
Dale Gray, IMF, Monetary and Capital
Markets Department
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
Traditional Analysis
Contingent Claims Analysis (CCA)
Applications of CCA:
• Macro Financial CCA: Non-linear Risk
Transmission
• Estimating Government Contingent Liabilities
• Systemic Risk Analysis (Application to US)
• Sovereign CCA models and Sovereign-Banking
Destabilization Spirals
• Integrated Policy Analysis
2
Outline of Presentation (cont.)
Presentation is based on material in:
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.
As well as:
• Book: Macrofinancial Risk Analysis; other IMF, JOIM, Annual
Review of Financial Economics (2009 and 2012) and related
papers
• Financial Risk Indicators in Monetary Policy Models (joint work
with Central Bank of Chile IMF WP 11/228)
• International Transmission of Macro, Sovereign, Financial, and
Corporate Distress (joint work with ECB CCA- GVAR Model of EU)
3
Traditional Analysis is Incomplete



Traditional macroeconomic and banking models do not
adequately measure risk exposures of financial
institutions and sovereigns and cannot be used to
understand the transmission and amplification of risk
within and between balance sheets in the economy.
Traditional macroeconomic analysis of the government
and central bank is almost entirely flow or accounting
balance sheet based. Sovereign debt analyses focus on
debt sustainability (stocks, flows and debt to GDP), not
sovereign risk exposures (contingent liabilities, expected
losses on sovereign debt).
A fundamental point is that accounting balance sheets or
a flow-of-funds do not indicate risk exposures, which are
forward-looking.
4
CCA Balance Sheet Models and
Macroeconomic Models




Macroeconomic models are largely based on flow
and accounting balance sheets, geared to try to
forecast the mean of macro variables (i.e. first
moment)
Finance measures risk from stochastic assets
relative to threshold (second and third moments
critical to risk indicators).
CCA is an excellent tool for analyzing
macrofinancial linkages
Time pattern of CCA risk indicators can be linked
to macroeconomic variables and to monetary
policy, DSGE, and other models
5
Added Dimension of Risk Indicators CCA Risk Analytics
Models to Spectrum of Macroeconomic Models
Risk Analytics Models
CCA, Credit Risk
Macroeconomic Theory Based
RBC, GE
IS-LM
DSGE, MPM
VAR
Data Based Macroeconomic Models
6
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
7
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
8
CCA is Generalization of Black, Scholes, Merton Option
Pricing Theory






Liabilities derive their value from assets
Assets are stochastic (changes driven by income flows, asset
sales, or changes in value, including credit risk/guarantees)
Uncertain 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
due to default which can be measured with an implicit put
option.
Key elements are Asset value (A) at time 0, asset return
volatility (σ), default barrier (B = PV of promised payments
on debt, time horizon (T) and risk-free rate (r).
Note : one does not have to know the expected returns to use
CCA/Merton models for valuation of liabilities!
9
Tradeoffs between Market Capitalization, Market Value
of Assets and Default Probability (Moody’s KMV data)
CITIGROUP EXAMPLE: From Sept 9, 2008 to March 9, 2009,
Market Capitalization fell from $125 bn to $6 bn, Assets
declined and Default Probability went from 0.5% to 24%
100,000
80,000
60,000
40,000
20,000
0
2,100,000 2,000,000 1,900,000 1,800,000 1,700,000 1,600,000 1,500,000 1,400,000
Market Value of Assets (million $)
Market Capitalization (million $)
120,000
Market Capitalization (million $)
140,000
140,000
120,000
100,000
80,000
60,000
40,000
20,000
0
0
5
10
15
20
30
25
EDF, One Year Default Probability in Percent
To get back to a BBB+ rating (0.3% EDF) combinations of
capital injection, asset guarantees, debt-equity swaps can
be evaluated; during TARP 107bn in capital was raised
10
CCA is Based on Proven, Well-tested
Calibration Techniques
Tools and techniques for calibrating CCA balance
sheets of corporates and financial institutions
are decades old (as described in HBR 2008
paper). Even commercial sources available
(e.g. Moody’sKMV provide CCA risk indicators
daily for 40,000 firms and financial institutions
in over 60 countries)
Extensions to sovereigns and economy-wide risk
transmission is becoming widespread
Over 20 countries have been calibrated; 35
sovereigns; CCA now included in several IMF
FSAPs; recent integration into DSGE/monetary
policy models and CCA GVAR (Global and EU)
11
As a result of the crisis, CCA concepts have
now found their way into the mainstream
The structural CCA model, with its
embedded fundamental volatility,
endogenously changes as values change
(e.g. shocks to assets endogenously
change values of equity and risky debt
and credit risk premiums)
It helps explain complex risk, especially
expected losses in financial system and
“insidiousness” of risk exposures where
small changes in value can lead to very
large changes in risk due to convexity!
12
CCA has robustness
It does not depend on expected returns;
It does not depend on preferences;
It is designed to be modular (can be
integrated with many models of
consumption and investment);
Retains the (endogenous) non-linearity of
values and risk exposures, and can
linked to macro models in different
ways.
13
For the remainder if my presentation I will describe
several ways CCA can be applied to enhance financial
stability and macroeconomic analysis
1. Non-linear risk transmission across sectors of an
economy
2. Estimating government contingent liabilities
3. Systemic CCA: Measuring Financial Sector Tail-risk
Losses And Contingent Liabilities
4. Applying CCA To The Sovereign Balance Sheet;
Framework For Sovereign-Bank Feedback
Destabilization Processes
5. Unified Macrofinancial Policy Framework
Further Applications
14
Balance Sheets for the Economic Sectors.
ASSETS
1. NON-LINEAR RISK
TRANSMISSION
ACROSS SECTORS OF
AN ECONOMY
Risky debt of
households and
corporates are assets
on financial institution
balance sheets and
implicit and explicit
guarantees of bank’s
liabilities are on the
sovereign balance
sheet
LIABILITIES
CORPORATE SECTOR
Debt
Corporate Assets
Equity
FINANCIAL SECTOR
Loans and other Assets
(including loans to corporates,
households and sovereign)
Debt and Deposits
Equity
Financial Guarantees
HOUSEHOLD SECTOR
Household Assets
(including household income and
savings in the form of deposits and
other financial assets)
Net Worth (Subsidiary BS)
Household Real Estate Assets
Household Net Worth – Claim on
Household Assets
Consumption is a “Dividend”
Payment out of Asset
Associated With this Claim
Household Mortgage and Debt
Net Worth (Subsidiary BS)
SOVEREIGN SECTOR (Government and Monetary Authorities)
Foreign Currency Reserves
Financial Guarantees
Net Fiscal Asset
(PV of taxes minus expenditures)
Foreign-currency Debt
Other Public Assets
Source: Gray, Merton and Bodie (2008).
Base Money and
Local-currency Debt
15
CCA Balance Sheet:
Assets Minus Liabilities equal Zero
CCA Balance Sheet
Assets
For a sector,
sub-sector
or
individual
institution
+or - Implicit or Explicit
Guarantees {Implicit Put
Options}
minus
Equity / Jr. Claim
{Implicit Call Option}
minus
(Default-free Value of Debt
– Implicit Put Option)
=0
16
Stylized Interlinked CCA Balance Sheets for an Economy
Sectors of an economy can be viewed as interconnected risk-adjusted balance
sheets with portfolios of assets, liabilities, and guarantees—explicit and implicit.
Corpo
rates
Households
H BS
Asset
AC
AFIN
H RE
AH , RE
 AL
Financial
Sector
AF
Sovereign
Govt.
PV (T  G )
MA
RFX 
 AG ,Other
 EG 
 EH , RE
ContingentAssets
& Liab
Equity/Jr. & Sub.
Claims
 EC
 EH
Foreign
AMA,Other
 EH , RE
 PF
 G PF
 EF
 EG
 cH
M BM
 BSLC  iSLC
 PSLC
Senior Claims
(Default Barrier)
Put
Sum
 BC
 BH , RE
iC
iH , RE
 PC
0
 PH
0
0
BF  i f
 BSFX  iSFX
Foreign
(1   ) PF
0
 PSFX
Claims
0
0
0
17
Economy-wide CCA Balance Sheet Models
Capture Non-linear Risk Transmission


Interlinked implicit options result in
compound options that exhibit highly nonlinear risk transmission, as seen a variety
of financial crises
Note that if asset volatility in CCA balance
sheets is set to zero:
– Implicit put options go to zero,
– Macroeconomic accounting balance sheets and
traditional flow-of-funds are the result
– Measurement of (non-linear) risk transmission is not
possible using macroeconomic flow or accounting
frameworks when fundamental structural volatility is
ignored (i.e. = 0).
18
Examples of Channels of Risk Transmission
Corporates/Households
Banks
Banks
Sovereigns
Sovereigns
Sov Debt Holders
Financial System Risk
Financial Sector
GDP Growth
Sovereign
GDP Growth
19
Example of Transmission of Risk from Corporate Sector
to Banks to Sovereigns: Case of Thailand
My initial work was motivated to try to understand Asia crisis.
First model was a simple: four corporate sectors, two bank
sectors, sovereign model for the Thai economy in the 1990s.
Large amounts of foreign debt and pegged exchange rate. This
simple model showed:
Corporate risk is embedded in the banking risk. The sensitivity
(delta) and convexity (gamma) of banking sector risk increased
as the exchange rate depreciates (insidious risk).
20
Example of Transmission of Risk from Corporate Sector
to Banks to Sovereigns: Case of Thailand (cont.)
In fact, by using forward exchange rate level and
volatility plus balance sheet data (available at the
time), the probability distribution of financial sector
losses was 25% of GDP, BEFORE the spot rate moved
from its pegged level!
Actual losses reached around 40% of GDP
21
2. ESTIMATING GOVERNMENT CONTINGENT
LIABILITIES AND RISK TRANSMISSION TO
SOVEREIGN



Implied credit spreads derived from CCA
models (i.e. derived from equity
information) are frequently higher than
the observed market CDS
This is due to the depressing effect of
implicit and explicit government
guarantees
The ‘market implied’ guarantees (implicit
put option values) can be estimated using
CCA and observed CDS
22
Citigroup: Example of Implicit Put Option Value
Extracted from CDS vs from CCA model and Estimated
Contingent Liability (billions of US$)
800
700
Implicit Put Option Value
from CDS
600
500
Implicit Put Option Value
from CCA (FVCDS)
400
300
200
450
100
400
0
350 30-Apr-11
30-Apr-07 30-Apr-08 30-Apr-09 30-Apr-10
300
Estimated
“market
implied"
government
continent
liability
250
200
150
100
50
0
30-Apr-07
30-Apr-08
30-Apr-09
30-Apr-10
30-Apr-11
23
Transfer of Risk: Ireland CDS spreads of banks
declined following guarantees in 4Q 2008 and
sovereign spread increases
400
Irish Banks and Sovereign CDS Spreads
(In basis points)
350
Allied Irish Bank
300
Bank of Ireland
Irish Government
250
200
150
100
50
0
Jun-07
Sep-07
Source: Bloomberg L.P.
Dec-07
Mar-08
Jun-08
Sep-08
Dec-08
Transfer of Risk to Sovereign: Austria Example – CCA
Banking Contingent Liabilities vs Sovereign CDS spread
Total Contingent Liabilities vs 5-yr Sovereign CDS Spread
180
160
250
120
200
100
150
80
100
60
50
40
Total Contingent Liabilities (simple sum)
4/
7/
20
09
2/
24
/2
00
9
1/
13
/2
00
9
10
/1
4/
20
08
11
/2
5/
20
08
0
9/
2/
20
08
20
7/
21
/2
00
8
Billions of US $
140
Sovereign CDS Spread (Basis Points)
300
Sovereign 5-yr CDS Spread
25
Note how CCA financial sector risk
indicators (aggregated financial
institution CCA indicator related to
expected losses) provides:



A robust measure of financial sector
systemic risk;
Frequently a leading indicator of credit
growth and GDP/output gap;
Provides a quantitative measure of
government contingent liabilities, which
affects sovereign risk (e.g. spreads)
26
3. 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
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 the time-varying and non-linear dependence
(iii) measures of financial sector tail risk (95%
VaR or Expected Shortfall)
(See Gray and Jobst Swedish Economic Review
2011, and 2009, 2010, & forthcoming)
27
Extreme Tail Risk Total Expected Losses including
dependence structure, 36 largest US financial
institutions, 95th percentile
Lehman
Collapse
In US dollar billions
4,000
Total Cont. Liab. (sum of individual expected losses)
Total Cont. Liab. (ES, 95th percentile)
3,000
2,000
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
1,000
Source: IMF US Financial Sector Assessment Program Stress Testing Technical Note 2010
28
Extreme Tail Risk Government Contingent Liabilities
including dependence structure, 36 largest financial
institutions, 95th percentile
Source: IMF US Financial Sector Assessment Program Stress Testing Technical Note 2010
29
Joint EL Ratio =
Joint Expected Losses (from
Systemic 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)
30
4. APPLYING CCA TO THE SOVEREIGN
BALANCE SHEET; FRAMEWORK FOR
SOVEREIGN BANK FEEDBACK
DESTABILIZATION PROCESSES
1.
Calibrating sovereign balance sheets for emerging
market sovereigns with significant foreign
currency debt (see Gray, Merton, Bodie 2007 JOIM
paper; Gapen et al. IMF Staff Papers 55 #1)
2. Calibrating developed country sovereign balance
sheets or government balance sheets in euro area.
Calibration uses term structure of CDS/bonds plus
debt data to infer sovereign assets, volatility and
skew (see Gray, Jobst Swedish Economic Review, page
24 and Appendix 3).
31
Sovereign CCA Balance Sheet, Contingent
Liabilities, Joint Bank-Sovereign Stress Testing
Sovereign, or Government, CCA model gives implied
sovereign asset value and volatility, whose components
can be estimated as shown below:
ASov = Reserves + PV (primary fiscal surplus) – Contingent
Liabilities to Financial sector + Other (residual)
Sovereign spreads = f (sovereign assets, volatility, debt
default barrier, time horizon, risk-free rate)
Joint stress testing of banks and sovereigns can be carried
out: macro variables affect bank CCA, and thus contingent
liabilities, and fiscal surplus and in turn sovereign spreads
(Swedish Economic Review paper shows example on page 24 and 25 of base
and adverse stress test of bank expected losses and sovereign spreads.)
32
Linkages between Stylized Financial Sector and
Sovereign CCA Balance Sheets
FINANCIAL SECTOR
GOVERNMENT
ASSETS
Assets (including government
debt)/Loans/Other Assets
+ Liquid Assets/Reserves
+ Asset Guarantees
LIABILITIES
- Equity (non-government)
- Equity (government-owned)
- Default-free Debt & Deposits
+ (1-α) *Expected Losses due to
Default
- Present value of Guarantee fees
ASSETS MINUS LIABILITIES
0
Present value of (Fiscal Surplus
and Guarantee fees)
+ Equity (government owned)
+ Other Assets
Credit owed to Central Bank
- Asset Guarantees
- α* Expected Losses due to Default
-
- Default-free Sovereign Debt
+ Expected Losses due to Sovereign Default
0
Source: Gray Jobst 2010, and Swedish Economic Review 2011
The circular linkage of bank expected losses and expected losses on
33
sovereign debt can create an insidious destabilization spiral
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
SOVEREIGN
I. Increase in contingent
liabilities of govt.
B. Increase in bank
funding costs
BANKS
I. Increase in
contingent
liabilities of
govt.
C. Erosion in potential
for official support
D. Mark-to-market fall in
value of govt. bonds
held by foreign banks
F. Contagion channels
(A, B, & C as above)
G. Rise in counterparty credit risk
BANKS
H. Withdrawal
of funding for
risky banks
34
CCA Risk Exposures Facilitate Quantitative Analysis of Risk
Mitigation Policy Options

Banks:



Portfolio adjustment/ring-fenced asset guarantees
asset volatility
 Lower

Debt to equity conversion  lower default barrier, higher equity

Guarantees on bank debt
 lower borrowing spreads
Sovereigns:

Increase debt maturity

Fiscal adjustment (“Fiscal Compact”)  higher sovereign assets


Increase bank capital  higher assets, lower expected losses
 lower default barrier
Guarantees/insurance on sovereign debt
spreads
 lower sovereign
Supranational:


Debt purchases by public entity (ECB, EFSF, ESM, other)  lower
sovereign spreads
Eurobonds  lower sovereign spreads, risk diversification
35
5. UNIFIED MACROFINANCE FRAMEWORK
Targets: Inflation, GDP,
Financial System Credit Risk, Sovereign Credit Risk
Financial Stability
Policies:
Fiscal and Debt
Policies:
Monetary Policies:
• Capital Adequacy
• Policy Rate
• Fiscal Policy
• Financial Regulations
• Liquidity Facilities
• Debt Management
• Economic Capital
• Quantitative Actions
• Reserve Management
Household
CCA
Balance
Sheet(s)
Corporate
Sector CCA
Balance
Sheet(s)
Financial
Sector
CCA
Model
Financial System
Credit Risk
Indicator
Monetary
Policy
Model
Sovereign
Credit Risk
Indicator
Sovereign
CCA Balance
Sheet
Model
Global
Market
Claims
on
Sovereign
Interest Rate Term Structure
Guarantees
Sovereign Equity Claims (from Capital Injections)
36
Traditional Flow and Accounting Framework
No Risk-Adjusted Balance Sheets (Asset Volatility = 0)
No Credit Risk or Guarantees; No Risk Exposures
Financial Stability
Policies:
Monetary Policies:
Fiscal and Debt
Policies:
• Capital Adequacy
• Policy Rate
• Fiscal Policy
• Financial Regulations
• Liquidity Facilities
• Debt Management
• Quantitative Actions
• Reserve Management
Household
Accounting
Balance
Sheet(s)
Credit Flows
Corporate
Accounting
Balance
Sheet(s)
Bank
Accounting
Balance
Sheets
Monetary
Policy
Model
Government
Accounts
Flow of
Funds
Global
Market
Flows
Interest Rates
Capital Injections
37
FURTHER APPLICATIONS – New risk-adjusted GDP and
external account measures (from economy-wide CCA
balance sheets, slide 17)
Solving for the put option in the financial sector one can see the put option depends on the
implicit put options in other sectors. It is the compound nature of this put option which can
lead to sudden non-linear increases in value.
PF  EF  BF  AF  EF  BF   DCF ( BC  PC )   DLCF ( BGLC  PGLC )   DH ( BH  PH )
Combining all the components of the junior claims or call option values across the economy
(households, government, and foreign sector) gives the CCA-based Economic Output
EOCCA =

1
EH  j EH  ...  n EH   EG  f EC EC  f EF EF .
The sum of all foreign claims across the sectors is defined as the risk adjusted external
account.
Risk Adjusted External Account  f EC EC  f EF EF  f DC ( BC  PC )
 f DF ( BF  (1   ) PF )  fGLCH ( BGLC  PGLC )
See 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
38
FURTHER APPLICATIONS (cont.) - Integrating
Financial System Risk Indicators into Monetary
Policy Models
GDP is affected by financial stability in the banking
system via:



Financial accelerator links;
Financial distress in banks and bank’s borrowers
reduces lending as borrower’s credit risk increases;
Explicit inclusion of CCA banking system credit risk
indicator in monetary policy models in the output gap
equation.
Uses a simple two-module framework:
1. Central Bank of Chile Macro Monetary Policy
Model.
2. CCA Financial System Module.
See Garcia et al. 2011 “Incorporating Financial Sector into Monetary Policy Models: Case
of Chile” IMF WP/11/228
39
Including CCA Banking Risk Indicator in Bank
of Chile Monetary Policy Model: Conclusions
Robust efficient frontier
(inflation volatility vs
output volatility) A
stronger reaction of
interest rates to financial
risk indicator reduces
inflation volatility and
output volatility.
Base model reaction to dtd: 0.5, 1.0 ,1.5
6.0
DTD: 0.5
Output volatility
A simple, but powerful model
for monetary policy
including financial sector
risk. Empirical evidence
supports the model.
Impulse Responses
behave according to
theory.
DTD: 1.0
5.0
DTD: 1.5
4.0
3.0
2.0
3.0
4.0
5.0
6.0
7.0
Inflation volatility
40
FURTHER APPLICATIONS (cont.) - Ongoing Joint work
with ECB – CCA-GVAR for EU
(Joint work with ECB: Marco Gross, Matthias Sydow, and
Joan Paredes)
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 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.
41
CCA-GVAR model framework
CCA bank by bank risk
indicators (63)
CCA sovereign credit
risk indicators (16)
GDP data (16 series)
Stock market index (16)
Other
CCA banking system
risk indicators (16)
Weighting Matrices
Scenarios and Shock Origins
GVAR
Model
Copula Simulation
(16 local
country models)
Scenario Responses
Banking Module
Banking system responses
Bank by bank output results :
- Funding cost impacts
- Expected Losses
- Implied market capital
impact
- Government
contingent liabilities
GDP growth IRs
Stock market IRs
Banking system IRs
(ELRs and spreads)
Sovereign IRs (ELRs and
spreads)
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
42
Conclusions for CCA GVAR Model

Integrated forward-looking banking system, corporate
and sovereign risk indicators in a GVAR with
macroeconomy and financial markets;

Large panel of banks, sovereigns, macro, financial
markets all fully endogenous;

Stable global model provides meaningful responses in
terms of directions and magnitude;

CCA captures nonlinear features related to bank capital,
funding cost and sovereign risk;

Allows a range of policy options to be modeled and their
risk mitigation impact quantified.
43
Additional references:
Macrofinancial Risk Analysis, Gray and Malone (Wiley Finance book Foreword by Robert Merton)
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.
Chen, Q., D. Gray, P. N’Diaye, H. Oura 2010 “International Transmission of Bank and Corporate Distress”
IMF Working Paper No. 10/124 (Washington: International Monetary Fund).
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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