Measuring and Monitoring Financial Systemic Risk
Presentation to G-20 Conference on “Financial Systemic Risk” September 27-28 2012 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
2
III. Using Contingent Claims Analysis (CCA) for Financial, and Sovereign, Systemic Risk Analysis
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)
A PRACTICAL APPROACH TO SYSTEMIC RISK MONITORING* – IMF Monetary and Capital Markets Department
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
Monetary and Capital Markets Department
2-page templates to describe each tool:
– main characteristics
– strengths and weaknesses
– uses
– example
Toolkit Coverage:
– Types of risks: credit, liquidity, market risks;
– Types of tools: single risk indicators, macro-financial, 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).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 market-based 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
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 multi-round 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� 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
AccountingBalance Sheet
Market Equity-based
Contingent
Claims Analysis
(CCA); MKMV
Advanced CCA -
with Govt
Equity levels
and returns,
Equity option
implied
Traditional
Balance
Sheet
Indicators
Market Debt-based (CDS & bond spreads)
with Govt
contingent
liabilities
implied
volatility and
skew
CDS or Bond implied Probability
of Default (PoD)
CDS/spread
implied PoD
Indicators
Distress
Insurance
Premium
Systemic Risk Models for Multiple Entities
AccountingBalance Sheet
Market Equity-based
Merton-type
CCA Joint
Risk
Systemic
Equity Joint Tail Risk
(from returns- SES
and MES)
Equity option
implied joint risk
Aggregate
FSIs
Network
Interbank Exposures
Merton-based
Network
Lo –Sherman model
Market Debt-based (CDS & bond spreads)
Systemic
CCA
implied joint risk
CDS CoRisk CDS –Joint
Probability of
Distress (CoPoD,
BSI)
CDS/spread
implied
JPoD
Systemic
Distress
Insurance
Premium CoVaR
Core Concept of Contingent Claims Analysis (CCA): Merton Model
Assets
Equity
or Jr Claims
• Value of liabilities derived from value of assets.• Liabilities have
III. Using Contingent Claims Analysis (CCA) for Financial, and Sovereign, Systemic Risk Analysis
11
Assets = Equity + Risky Debt
= Equity + Default-Free Debt – Expected Loss
= Implicit Call Option + Default-Free Debt – Implicit Put Option
Risky Debt
• Liabilities have different seniority.• Randomness in asset value.
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 � 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 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*LGD=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
y = 0.0272x-0.265
R² = 0.8541
0
0.02
0.04
0.06
0.08
0.1
EDF (%)
y = 0.9854x-0.494
R² = 0.7648
0%
2%
4%
6%
8%
10%
Mkt Cap/Assets Ratio (CCACR in %)
15
y = 0.1217x1.0815
R² = 0.9987
0
200
400
600
800
1000
1200
0 1000 2000 3000 4000 5000
EL Ratio (in bps)
y = 320.9x0.5
R² = 0.829
0
200
400
600
800
1000
1200
012345678
Fair Value Spread (bps)
0
024680%
0 1000 2000 3000 4000 5000
Low Risk Zone: CCACR>2.5%, EDF<1.5%, EL<1800 bps, FVSpread<380 bps
Based on Moody’sKMVCreditEdge data
and IMF calculations
CCA-based FV credit spreads greater than observed CDS spreads (in bps)
CCA provides Estimates of “Market-Implied” Government Contingent Liability (Citigroup example 2007-2011)
200
400
600
800
1000
1200
1400
1600
FVCDS
MARKIT CDS
16
0
200
0
50
100
150
200
250
300
350
400
450
30-Apr-07 30-Apr-08 30-Apr-09 30-Apr-10 30-Apr-11
Estimated “market implied"
government contingent
liability (billion US $)
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,
17
(i) the banking system expected losses, and,
(ii) government’s contingent liabilities accounting for dependence
(iii) measures of financial sector tail risk (95% VaRor Expected Shortfall)
(iv) macro-factor model linked to CCA to stress test systemic tail risk.
(See Gray and Jobst 2009, 2010, 2011, and forthcoming)
Extreme Tail Risk – US Financial System -Joint Total Expected Loss Value including dependence structure, 36 largest US financial institutions, 95th percentile
3,000
4,000
In U
S d
oll
ar b
illi
on
s
Total Cont. Liab. (sum of individual expected losses)
Total Cont. Liab. (ES, 95th percentile)
Lehman
Collapse
18
0
1,000
2,000
Ap
r-0
7
Ma
y-0
7
Jun
-07
Jul-
07
Au
g-0
7
Se
p-0
7
Oc
t-0
7
No
v-0
7
De
c-0
7
Jan
-08
Fe
b-0
8
Ma
r-0
8
Ap
r-0
8
Ma
y-0
8
Jun
-08
Jul-
08
Au
g-0
8
Se
p-0
8
Oc
t-0
8
No
v-0
8
De
c-0
8
Jan
-09
Fe
b-0
9
Ma
r-0
9
Ap
r-0
9
Ma
y-0
9
Jun
-09
Jul-
09
Au
g-0
9
Se
p-0
9
Oc
t-0
9
No
v-0
9
De
c-0
9
Jan
-10
In U
S d
oll
ar b
illi
on
s
Just after Lehman there was a 5% chance of $3 trillion losses in financial system over a
one year horizon!
Extreme Tail Risk – US Government Contingent Liabilities including dependence structure, 36 largest financial institutions, 95th percentile
19
(Contributions of individual institutions can also be measured.)
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)
Jo
int
EL
Rati
o =
Jo
int
Exp
ecte
d L
osse
s (
fro
m
Sys
tem
ic C
CA
)/ A
gg
rega
te
Ma
rke
t C
apitaliz
atio
n
Systemic Risk Assessment
20
Aggregate MCAR =
Aggregate Market Capitalization/
Aggregate Implied Assets
Aggregate Fair Value Credit
Spread
Jo
int
Exp
ecte
d L
osse
s (
f
Sys
tem
ic C
CA
Imp
lied
Asset
Vo
lati
lity
(Sam
ple
Me
dia
n)
Jobst and Gray (2012)
Spillovers from the Sovereign to the Banks and Banks to Sovereigns
DOMESTIC
SOVEREIGN
BANKS
A. Mark-to-market fall in
value of govt bonds
held by local banks
B. Increase in bank
funding costs
I. Increase in
contingent
liabilities of
Latest Stage of Crisis: Destabilizing Feedback Loops between Sovereigns and Banks are Amplifying Risk
21
FOREIGN
SOVEREIGN
C. Erosion in potential
for official support
D. Mark-to-market fall in
value of govt. bonds
held by foreign banks
E. Similar
sovereigns come
under pressure
F. Contagion channels
(A, B, & C as above)
G. Rise in counter-
party credit risk
H. Withdrawal
of funding for
risky banksBANKS
liabilities of
govt.
I. Increase in contingent
liabilities of govt.
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)
GDP data (16 series)
Stock market index (16)
Other Scenarios and Shock Origins
CCA banking system
risk indicators (16)
CCA corporate credit
risk indicators (16)
23
Copula Simulation
GVAR
Model (16 local
country models)
Scenarios and Shock Origins
Weighting Matrices
0
20
40
60
80
100
120
140
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
14.0%
DE IE DK NL FR PT AT ES IT CH UK BE SE NO US GR
EL (l) and CDS (r) SOVEREIGNS
0
50
100
150
200
250
300
350
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
ES IT SE NO NL GR FR CH IE AT DK PT BE DE UK US
EL (l) and CDS (r) BANKS
0
5
10
15
20
25
0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
6.0%
7.0%
8.0%
ES BE IT PT CH DK SE DE FR US UK NO NL AT GR IE
EL (l) and CDS (r) CORPORATE
-0.70%
-0.60%
-0.50%
-0.40%
-0.30%
-0.20%
-0.10%
0.00%
IE UK DK IT ES CH DE SE NL US FR AT BE PT NO GR
GDP
Scenario responses are fed into banking, sovereign, corporate modules to get secondary outputs
Scenario Responses
GDP growth IRs
Stock market IRs
Banking system IRs
Corporate sector IRs Banking Module
Corporate Module Corporate output results :
- Expected Losses
- Credit Spreads
24
Corporate sector IRs
Sovereign IRs
EU and Euro Zone Aggregate banking and sovereign
debt expected losses
Aggregate bank capital impact
Sovereign Module Sovereign output results :
- Expected Losses
- Credit Spreads
- Other
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
Fiscal and Growth
Module - Contingent liabilities
- Borrowing cost changes
- Debt to GDP changes
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 ���� lower bank borrowing costs
� Ring-fenced asset guarantees ���� Lower asset volatility
� Sovereigns:
25
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
Thank you
Appendix
Systemic Risk Monitoring Tools and
(Contact information: [email protected])
Systemic Risk Monitoring Tools and Bibliography
References for CCA/Systemic Risk Models
26
Summary of tools for Systemic Risk MonitoringTools
Inst
itu
tio
ns
Ma
rke
ts
Se
cto
rs
Fre
qu
en
cy
Ty
pe
of
Da
ta
Low
In
com
e
Em
erg
ing
Ad
va
nce
d
Q1
Q2
Q3
Q4
Q5
Q6
Th
resh
old
s
Ea
rly
Wa
rnin
g
Imp
act
of
cris
is
Am
pli
fica
tio
n
CoVaR Y N Financial HighAsset prices and
balance sheet dataLimited Y Y Y Y Y Y Y N
CoPoD/BSI Y N Financial High Asset prices N Limited Y Y Y Y N Y Y N
Volatility Spillovers Y N Financial High Asset prices Limited Y Y Y Y Y Y Y N
Distress Spillovers Y Y Financial High Asset prices Limited Y Y Y Y Y N Y Y N
Market-Based
Probability of Y NFinancial and
HighAsset prices and
Limited Y Y Y N Y N N
CoverageApplicability across
CountriesData Requirements
Applicability across
Questions
Additional
Characteristics
Probability of
Default
Y Ncorporate
Highbalance sheet data
Limited Y Y Y N Y N N
DSA N NExternal and
public Low BoP and fiscal data Y Y Y Y Y Y N Y N
Asset Price N Y N MediumAsset prices and
cash flow dataLimited Limited Y Y Y Y Y Y N Y
Balance Sheet
AnalysisN N
All main
sectorsLow
Sectoral balance
sheet dataY Y Y Y Y N Y N Y
Systemic CCA Y N Financial HighAsset prices and
balance sheet dataLimited Y Y Y Y Y Y Y N Y Y N
Cross-Border
InterconectednessY N Banking Low
Cross-border
banking exposure
and balance sheet
data
Limited Y Y Y Y Y Y Y N
Summary of tools (cont’d)Tools
Inst
itu
tio
ns
Ma
rke
ts
Se
cto
rs
Fre
qu
en
cy
Ty
pe
of
Da
ta
Low
In
com
e
Em
erg
ing
Ad
va
nce
d
Q1
Q2
Q3
Q4
Q5
Q6
Th
resh
old
s
Ea
rly
Wa
rnin
g
Imp
act
of
cris
is
Am
pli
fica
tio
n
Systemic liquidity Y N Financial HighAsset prices and
balance sheet dataN Limited Y Y Y N N Y N
Regime switching N Y Financial high Asset prices Y Y Y Y Y Y Y Y N
FSIs Y Y
Financial,
corporate and
household
LowCash flow and
balance sheet dataY Y Y Y Y Y N Y N N
Coverage Data RequirementsApplicability across
Countries
Applicability across
Questions
Additional
Characteristics
Thresholds Model N N Financial LowMacroeconomic
dataY Y Y Y Y Y Y Y Y N Y
Macro stress tests Y N Financial Asset prices and
balance sheet dataLimited Y Y Y Y Y Y N N Y Y
GDP at Risk N N N Low
Asset prices and
macroeconomic
data
Y Y Y Y Y Y Y Y Y
Credit to GDP-Based
Crisis Prediction
Model
N N Financial LowMacroeconomic
dataLimited Y Y Y Y Y Y Y Y N N
Crisis Prediction
ModelN N
Financial and
public Low
Macroeconomic
dataLimited Y Y Y Y Y Y Y Y N N
DSGE Model N N
Corporate
and
household
LowMacroeconomic
dataY Y Y Y Y Y N N Y 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.htmavailable at https://www.bis.org/publ/bcbs_wp21.htm
Benes, Jaromir, Michael Kumhof, and David Vavra, 2010, “Monetary Policy and Financial Stability in Emerging-Market 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 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”, ______–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 RiksbankEconomic 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
33
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.
Macrofinancial Risk Analysis
� Framework integrates risk-adjusted balance sheets using Contingent Claims Analysis (CCA) of financial institutions, corporates, and sovereigns together
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and sovereigns together and with macroeconomic and monetary policy models
� TOOLKIT FOR MACRO RISK ANALYSIS