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: Dgray@imf.org)

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