A Framework for Macro-financial Stress Testing CNBV (México) I Meeting on Financial Stability Mexico City November 3-4, 2011 *On temporal leave from the IMF. The views expressed in this presentation are those of the author and do not necessarily represent those of the IMF, CNBV or IMF,CNBV policy. Any errors remain attributable to the author
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A Framework forMacro-financial Stress Testingg
CNBV (México)
I Meeting on Financial Stability
Mexico CityNovember 3-4, 2011
*On temporal leave from the IMF. The views expressed in this presentation are those of the author and do not necessarily represent those of the IMF, CNBV or IMF,CNBV policy. Any errors remain attributable to the author
Outline
I. Objective and Modelling Framework.
I. Pillars I ‐II: Individual Bank Perspective
III. Pillars III‐IV‐V: Systemic Risk Perspective.
IV. ST with Second Round Effects.
V. Pillar V: Contagion.
2
Objective
Provide the CNBV a methodological framework for risk i dassessment in order to:
• Support the design policy to minimize the potential negative effects of macroeconomic‐ financial shocks in the Mexican financial systemfinancial system.
• Implement a risk based regulatory framework• Implement a risk‐based regulatory framework.
Individual Bank Perspective Systemic Macro-Financial Perspective
Market-BasedInformation
p
SupervisoryInformation
y p
Information
Pillar III:Systemic
Risk-Based ST
Pillar IV:Financial Stability
Indicators
Pillar V:ContagionIndicators
Pillar II:Enhanced
Ri k B d ST
Pillar I:Balance Sheet ST
Macro-Financial Scenarios
Macro-Financial Scenarios
Risk-Based ST
Macro-Financial Scenarios
MCSR•Portfolio growth and composition.
Liquidity Risk
Portfolio
Financial StabilityMeasures
Mx SubsBHCs
Portfolio Losses
MarketRisk
EL
•Financial Margin.
•Net Result
••Portfolio Multivariate
Density.•Unexpected Losses.
• Portfoliocomposition
• Significantrisk factors.
• VaR
EL
e-CAR •SVaRCAR
Modelling Contributions
• It is a comprehensive coverage: The methodology allows for the inclusion of banking and non‐banking financial institutions (FIs)/sectors.
• It captures contagion effects: It takes into account interlinkages (direct and indirect) amongst Fis.
• It captures changes across the economic cycle of distress dependence amongst FIs and sovereigns.dependence amongst FIs and sovereigns.
• It integrates complementary information: It uses micro‐f d d i d t d k t b d i f tifounded supervisory data and market‐based information.
• It incorporates a wide set of factors: It accounts for a wideIt incorporates a wide set of factors: It accounts for a wide set of macroeconomic and financial risk factors.
5
Main Points
• It provides robust estimations: It benefits from robust• It provides robust estimations: It benefits from robust estimation with restricted data (under the PIT criterion).
• It can be extended to capture second round effects: It allows to take into account second‐round effects and macro‐financial linkages.financial linkages.
• Framework implemented and scrutinized by supervisors d l k d h ldand Central Banks around the world.
C t C t ICAP L l C t MCategory Category ICAP Levels Category MeasuresCategory I ICAP≥10% No measuresCategory II 8%≤ICAP<10% The FI will abstain from realizingCategory II 8%≤ICAP<10% operations that will cause their ICAP to
fall below the levels requred by theCapitalization Rules.
Category III 7%≤ICAP<8% 1. Suspend all payments of dividends tostockholdersstockholders.
2. Suspend all compensations and bonuses to the director general of theFI.
3. Present a plan for restaurationsubject to various obligationssubject to various obligationsrequired by the CNBV
Category IV 4%≤ICAP<7% All measures required for FI undercategory are readily applicable forcategory IV. Aditionally the FI must askf th i ti b th CNBV t i t ifor authorization by the CNBV to invest in non-financial assets, open new branchesor realizing operations other than theones normally realized by the FI.
Category V ICAP≤4% All measures applicable for FI underCategory V ICAP≤4% ppCategory IV will be applied.
Banking Sector
23
24
25
eCAR Graphs
26
27
Pillars III-IV-V: Systemic Macro-Financial
PerspectivePerspective
Modeling Framework
Supervisory Information Market Information
Commercial BankingPoD
Pension FundsPoD
Mutual FundsPOD
Develpmt BkingPOD
Insurance CosPoD
BrokersPoD Others
EAD
Financial System´sMultivariate Density
LGD 0 . 2
SystemicLoss Simulation
Systemic StressIndicators
- 4- 2
02
4
- 4- 2
0
240
0 . 0 5
0 . 1
0 . 1 5
Systemic LossIndicators
Sovereign Risk
ContagionIndicators
Sovereign RiskAssessment
30
Marginal contribution toSystemic Risk
Distress Dependence
Segoviano and Goodhart (2009)
Distress dependence between institutions is incorporated via jointDistress dependence between institutions is incorporated via joint movements of their PoDs, which in turn move in tandem due to
h kh kIndirect LinksIndirect Links
Systemic shocksSystemic shocks Lending to common sectorsLending to common sectorsProprietaryProprietary TradesTrades
Contagion throughContagion throughIdi i Sh kIdi i Sh k
Direct LinksDirect LinksI tI t B k D it M k tB k D it M k t
The recent crisis underlined that proper estimation of distress dependence FI i fi i l i i l f fi i l bili
amongst FIs in a financial system is essential for financial stability assessment.
G dh S i d T (2004)Goodhart, Sunirand, Tsomocos (2004).31
The CIMDO Methodology
• Problem: ‘how to estimate P(A,B) if we have P(A) and P(B)?’( ) ( ) ( )
• We can assume a known parametric distribution (e.g. multivariate normal), and estimate/calibrate parameters using data on A and B, but it seldom fits the data…
• …or, we can try to “match” the data with a non‐parametric distribution ‐‐> CIMDO.
Advantages:
• Robust without imposing unrealistic parametric assumptions.
• It can be estimated from partial information: From PoDs on marginals, without the need to explicitly set correlation structuresto explicitly set correlation structures.
• It characterizes the full “distributional dependence”: Rather than just linear dependence (correlations) or relations in the first few moments.
• It embeds effects of changing macroeconomic conditions/shocks (via PoDs): It allows measurement of changes in dependence after shocks.
32Source: Segoviano (2006)
CIMDO‐Density
EmpiricalI f tiInformation
33
CIMDO-Density
34
CIMDO‐Copula
L t X d Y b t d i bl ith ti di t ib ti f ti F d H it lLet X and Y be two random variables with continuous distribution functions F and H respecitvely, then the Spearman Correlation of X and Y is defined and denoted by the following:
22
3),(12]),([12))(),((),(I
vuCI
dudvuvvuCYHXFYXS
2
35
Where and ρ(F(X),H(Y)) is the Pearson Correlation of the transformed uniformrandom variables F(X) and G(Y).
]1,0[]1,0[2 xI
Distress Dependence: CIMDO-Copula
CIMDO-Copula. (Segoviano, 2008)
Maintains the benefits of copula modeling butMaintains the benefits of copula modeling but
• Allows for changing dependence as empirical PoDsh hil t diti l t i l f tichange, while traditional parametric copula functions
assume it constant.
• Avoids copula choice problem.
• Outperforms commonly used parametric copula functions• Outperforms commonly used parametric copula functions under the PIT criterion.
R dil i l t bl ith il bl d t (P D )• Readily implementable with available data (PoDs).
36
PoDMarket InformationMarket Information
37
PoDs Graphs
38
Pillar III:Marginal Contribution to Systemic RiskMarginal Contribution to Systemic Risk
39
B1B4
B2
B3
B5
B3
40
Marginal Contribution to Systemic Risk:Mexico
Mexican Bank P Mexican Bank S
0.050.10.150.2
0.050.1
0.150.2
0.250.3
0.220.230.240.250.260.27
0.050.1
0.150.2
0.250.3
000 05
01/0
3/20
0701
/06/
2007
01/0
9/20
0701
/12/
2007
01/0
3/20
0801
/06/
2008
01/0
9/20
0801
/12/
2008
01/0
3/20
0901
/06/
2009
01/0
9/20
0901
/12/
2009
01/0
3/20
1001
/06/
2010
01/0
9/20
1001
/12/
2010
0.210.22
00.05
01/0
3/20
0701
/06/
2007
01/0
9/20
0701
/12/
2007
01/0
3/20
0801
/06/
2008
01/0
9/20
0801
/12/
2008
01/0
3/20
0901
/06/
2009
01/0
9/20
0901
/12/
2009
01/0
3/20
1001
/06/
2010
01/0
9/20
1001
/12/
2010
Tamaño PoD
Spearman Correlation Shapley ValueTamaño PoD
Spearman Correlation Shapley Value
41
Marginal Contribution to Systemic Risk:U.S
Marginal Contribution to Systemic Risk:g yIt takes into account of size and interconnectedness.
AIG Factors
0.014
0.016
0.35
0.4
Axi
s fo
r CI
0.008
0.01
0.012
0.2
0.25
0.3
Rig
ht
0.002
0.004
0.006
0.05
0.1
0.15
00
Dec
-07
Jan-
08
Feb-
08
Mar
-08
Apr
-08
May
-08
Jun-
08
Jul-0
8
Aug
-08
Sep
-08
Oct
-08
Nov
-08
Dec
-08
Jan-
09
Feb-
09
Mar
-09
MCSR POD AIG Spearman Corr AIG Contagion Index AIG
Objective: Definition of “risk zones” based on the joint interaction ofmacroeconomic and financial indicators (JPoD).(Segoviano and Malik (2011) IMF WP forthcoming).( g ( ) g)
Based on a Markov Switching VAR that allows to quantify:
• Probability of migrating between different “risk zones”.• The impact that different macro-financial shocks have on those probabilities.
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Thank You
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References
• Athanosopoulou, M., Segoviano, M., and Tieman A., (2011), “Banks’ Probability of Default: Which Methodology, When, and Why?”, IMF Working Paper (forthcoming).
Cá C G V S i M (2010) “S i S d Gl b l Ri k• Cáceres, C., Guzzo, V., Segoviano, M., (2010), “Sovereign Spreads: Global Risk Aversion, Contagion or Fundamentals?”, IMF Working Paper WP/10/120.
• Espinoza, R. and Segoviano, M. (2011). “Probabilities of Default and the Market Price of Risk in a Distressed Economy” IMF Working Paper WP/11/75of Risk in a Distressed Economy , IMF Working Paper WP/11/75.
• Goodhart, C., Hofmann, B. and Segoviano, M. (2004), “Bank Regulation and Macroeconomic Fluctuations,” Oxford Review of Economic Policy, Vol. 20, No. 4, pp. 591–615.
• Goodhart, C., Hofmann B., and Segoviano M., (2006), “Default, Credit Growth, and Asset Prices”, IMF Working Paper 06/223.
• Segoviano, M. (2006). “Consistent Information Multivariate Density Optimizing Methodology” Financial Markets Group Discussion Paper No 557Methodology . Financial Markets Group, Discussion Paper No. 557.
• Segoviano, M. and Goodhart, C. (2009). “Banking Stability Measures”, IMF WP/09/4.• Segoviano, M., (2006), “The Conditional Probability of Default Methodology,”
Financial Markets Group, London School of Economics, Discussion Paper 558.Financial Markets Group, London School of Economics, Discussion Paper 558.• Segoviano, M., (2011), “The CIMDO‐Copula. Robust Estimation of Default
Dependence under Data Restrictions”, IMF Working Paper (forthcoming).• Segoviano, M. and Padilla, P., (2006), “Portfolio Credit risk and Macroeconomic
Shocks: Applications to Stress Testing under Data Restricted Environments ” IMF
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Shocks: Applications to Stress Testing under Data Restricted Environments, IMF WP/06/283.