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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.
• Validate financial institutions (FIs) IRB models;• Validate financial institutions (FIs) IRB models;
3
Stress Test Modeling 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.
6
Implementation Map
Projects with I t ti l
IMF/GFSR
ECB /FSR
International Authorities
ECB /FSR
USA FSAP
India FSAP Banca D’Italia
Bank of Japan
Banque de France
Denmark FSAPBank of Jordan
Norges Bank
Central Bank of UAE
Canada FSAP
Central Bank of S Korea
Lithuania FSAPRisksbank (Sweden)
Deutshe BundesbankCentral Bank of UAE
Central Bank of Indonesia
Central Bank of S Korea
Central Bank of Croatia
Complementary Sources of Information
Supervisory Information Market Information
PD/LGD Credit Card
PD /LGDConsumption
Credit
InterbankFinancing Cost
Credit
PD/LGDMortgage
Loans
PD /LGDCorporate
Credit
PD/LGDPD/LGDCredit to States and
Municipalities
PD/LGDCredit to FIs
8
Complementary Sources of Information
Supervisory “Micro founded Information”
Assets Types MortgageConsumer
C dit C d
Portfolio Loss Distribution for
Financial I tit tiCredit Card
CommercialInstitutions
Merton TypeC dit D f lt S d P D
MarketInformation
Credit Default Swaps spreadsBond SpreadsOption Prices
PoDMarket
Information
Advantages- Actual Portfolio Composition
- Information of Concentration / Driver s effects
Disadvantages
- It does not consider off-balance sheet items
- It does not account for risk of complex derivatives- Signals market perceptions that might consider risks not captured by supervisory Data
- Daily data with no lags
- Can serve as early warning indicator of issues to be
It does not account for risk of complex derivatives
- Low frequency and lagged
- Market perceptions not always correct
- Issues with thin markets- Can serve as early warning indicator of issues to be explored by Supervisory data
Pillars I-II: Individual Bank Perspective
Balance Sheet Stress Test
CNBV: Base and Adverse Scenarios
CNBV Analysis
Credit AssumptionsLoss Given Default
Probabability of DefaultCAR &
CAR SensitivityIncrease in
LiabilitiesMonthly stocks
Flows, stock (quarterly)Liabilities
Macroeconomic AssumptionsGDP
Unemployment rateHousing Index
Increase in expected loss
Decrease in loan origination
Liability structurechanges
CapitalizationNet capital, Risk Weighted
Assets)ICAP
CAR (2013)
Financial AssumptionsTIIEFX
changesFunding structure
changesDecrease in
Financial marginIncrease in Adm. &Balance
Loan PortfolioCorporate
SMESta&Muns
Fi i l I tit ti
Bank: Base and Adverse Scenarios
Increase in Adm. & Promotionalexpenses
Other incomeeliminationFinancial
Monthly stock
Cash FlowInterest, loan payments
Financial InstitutionsRevolving credit
Non revolving creditProjections (Quarterly)
Balance sheetIncome statement
Capital RatioCredit Origination
intermediationelimination
Loan ProvisionExpected loss
Income StatementI t tCredit Origination Interest
ST of Individual FIs: Modeling Framework
Stressed PoDStressedMacro
Variables
LGD(CoPoD)
MacroeconomicScenarioDesign
Step 1 Step 2
Bank’sStressed Portfolio
Multivariate Density
0 . 1
0 . 1 5
0 . 2
Bank’s E t
Bank s Exposure to Asset type X
(CIMDO)- 4
- 20
24
- 4- 2
0
240
0 . 0 5
St 3
Exposure to Asset type Y
SimulationStressed
Economic Capital
Step 3
Stressed PLDSimulationp
(VaR)Step 4 12
Macroeconomic Scenarios
Macroeconomic Scenario
Defined jointly between IMF and CNBV in consultation with SHCP.
• Baseline: Consistent with WEO projections• Baseline: Consistent with WEO projections.
• Adverse: Consistent with last period of financial, macroeconomic Distress.
BASELINEVariable 07‐Dic 08‐Dic 09‐Dic 10‐Dic 11‐Dic 12‐Dic 13‐Dic
PERCENT CHANGE NOMINAL GDP Y/Y 10.84% 2.53% 3.50% 2.59% 6.58% 7.11% 6.67%INFLATION LEVEL Y/Y 3.76% 6.53% 3.57% 4.40% 3.51% 2.99% 2.99%UNEMPLOYMENT RATE LEVEL 3.54% 4.26% 5.33% 5.36% 4.50% 3.90% 3.50%TIIE28 LEVEL 7.93% 8.70% 4.92% 4.88% 4.85% 5.85% 6.48%
BASELINE
TIIE28 LEVEL 7.93% 8.70% 4.92% 4.88% 4.85% 5.85% 6.48%PERCENT CHANGE DOMESTIC CREDIT 16.40% 7.70% 14.88% 13.00% 12.86% 15.31% 16.53%PERCENT CHANGE HOUSING PRICES INDEX 6.08% 5.68% 3.64% 4.60% 4.52% 4.45% 4.95%IPC LEVEL 29,536.83 22,380.32 32,120.47 38,550.79 39,903.17 41,095.1530 42,325.79 CURRENCY RATE MXP/USD M/M 10.92 13.83 13.07 12.35 11.84 12.0200 12.19 VIMEX LEVEL 26.74 50.37 23.09 20.74 22.16 36.92 31.00
Variable 07‐Dic 08‐Dic 09‐Dic 10‐Dic 11‐Dic 12‐Dic 13‐DicPERCENT CHANGE NOMINAL GDP Y/Y 10.84% 2.53% 3.50% 2.59% 3.74% 3.29% 3.52%INFLATION LEVEL Y/Y 3.76% 6.53% 3.57% 4.40% 4.18% 5.98% 5.47%
ADVERSE
UNEMPLOYMENT RATE LEVEL 3.54% 4.26% 5.33% 5.36% 5.21% 5.06% 4.26%TIIE28 LEVEL 7.93% 8.70% 4.92% 4.88% 5.49% 7.12% 6.57%PERCENT CHANGE DOMESTIC CREDIT 16.40% 7.70% 14.88% 13.00% 9.91% 8.41% 16.05%PERCENT CHANGE HOUSING PRICES INDEX 6.08% 5.68% 3.64% 4.60% 4.45% 2.42% 4.92%IPC LEVEL 29,536.83 22,380.32 32,120.47 38,550.79 32,075.00 22,259.95 27,272.26
14
CURRENCY RATE MXP/USD M/M 10.92 13.83 13.07 12.35 12.33 13.8443 13.32 VIMEX LEVEL 26.74 50.37 23.09 20.74 26.12 50.37 36.92
Macroeconomic Scenario
15
PD Modelling:f(macro-financial factors)
Summary PD Modeling
TYPE OF CREDIT LOAN ADJUSTED R^2 VARIABLE SIGN LAG BETA P‐VALUECONSTANT ‐ ‐‐‐‐‐ 2.06617 0.0001PERCENT CHANGE NOMINAL GDP Y/Y ‐ 14 0.685319 0.0031INFLATION LEVEL Y/Y + 0 5.368116 0TIIE28 LEVEL + 1 3.029672 0.0835
MODELS
Corporatives 0.977125
VIMEX LEVEL + 5 0.000877 0AR(1) + ‐‐‐‐‐ 0.986921 0CONSTANT ‐ ‐‐‐‐‐ 1.229176 0DOMESTIC CREDIT/GDP ‐ 10 0.918431 0.0071INFLATION LEVEL Y/Y + 0 2.091662 0.007TIIE28 LEVEL + 11 1.664722 0.0002VIMEX LEVEL + 0 0.00055 0.0001
SME's 0.972802
0 0 00055 0 000AR(1) + ‐‐‐‐‐ 0.973609 0CONSTANT ‐ ‐‐‐‐‐ 1.543225 0.0113PERCENT CHANGE NOMINAL GDP Y/Y ‐ 14 0.877081 0.0955PERCENT CHANGE IPC M/M ‐ 1 0.094635 0.0144INFLATION LEVEL Y/Y + 0 7.273109 0.0033AR(1) + ‐‐‐‐‐ 0.980313 0CONSTANT ‐ ‐‐‐‐‐ 2.239624 0
Sta & Mun's 0.977609
CONSTANT 2.239624 0PERCENT CHANGE NOMINAL GDP Y/Y ‐ 14 0.710064 0PERCENT CHANGE IPC INDEX M/M ‐ 1 0.076908 0INFLATION LEVEL Y/Y + 0 5.192735 0TIIE28 LEVEL + 0 1.848745 0.0036VIMEX LEVEL + 5 0.001097 0AR(1) + ‐‐‐‐‐ 0.983825 0CONSTANT ‐ ‐‐‐‐‐ 1 175139 0 0001
Financial Institutions 0.976715
CONSTANT ‐ ‐‐‐‐‐ 1.175139 0.0001PERCENT CHANGE CURRENCY RATE MXP/USD M/M + 7 0.184682 0.0432DOMESTIC CREDIT/GDP ‐ 8 0.905785 0.0364INFLATION LEVEL Y/Y + 2 3.246666 0.0777TIIE28 LEVEL + 0 3.102642 0.0099AR(1) + ‐‐‐‐‐ 0.946654 0CONSTANT ‐ ‐‐‐‐‐ 0.788277 0.0001DOMESTIC CREDIT/GDP 6 1 31564 0 0147
Credit Card 0.983347
DOMESTIC CREDIT/GDP ‐ 6 1.31564 0.0147INFLATION LEVEL Y/Y + 3 2.390412 0.1577VIMEX LEVEL + 3 0.000942 0.0589AR(1) + ‐‐‐‐‐ 0.925927 0CONSTANT ‐ ‐‐‐‐‐ 2.173912 0PERCENT HOUSING PRICES INDEX ‐ 1 1.013343 0.0828PERCENT CHANGE IPC INDEX M/M ‐ 21 0.10354 0.03DOMESTIC CREDIT/GDP 2 1 769317 0 0023M t 0 960473
Not Revolving Consumption 0.963588
17
DOMESTIC CREDIT/GDP ‐ 2 1.769317 0.0023UNEMPLOYMENT RATE LEVEL + 8 9.828639 0.0107TIIE28 LEVEL + 6 13.27227 0AR(1) + ‐‐‐‐‐ 0.892155 0
Mortgagees 0.960473
PDs by Asset Class
10.00%
15.00%
20.00%
25.00%
0.00%
5.00%
1/09
/200
4
1/03
/200
5
1/09
/200
5
1/03
/200
6
1/09
/200
6
1/03
/200
7
1/09
/200
7
1/03
/200
8
1/09
/200
8
1/03
/200
9
1/09
/200
9
1/03
/201
0
1/09
/201
0
1/03
/201
1
1/09
/201
1
1/03
/201
2
1/09
/201
2
1/03
/201
3
1/09
/201
3
01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01
PD's SYSTEM BASELINE Revolving Credit
PD's SYSTEM ADVERSE Revolving Credit
18
PDs by Asset Class
10.00%
15.00%
20.00%
25.00%
0.00%
5.00%
1/09
/200
4
1/03
/200
5
1/09
/200
5
1/03
/200
6
1/09
/200
6
1/03
/200
7
1/09
/200
7
1/03
/200
8
1/09
/200
8
1/03
/200
9
1/09
/200
9
1/03
/201
0
1/09
/201
0
1/03
/201
1
1/09
/201
1
1/03
/201
2
1/09
/201
2
1/03
/201
3
1/09
/201
3
01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01
PD's SYSTEM BASELINE Revolving Credit
PD's SYSTEM ADVERSE Revolving Credit
19
PDs by Asset Class
20
Results
21
Regulatory Capital Ratio Measures
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
Idiosyncratic ShocksIdiosyncratic Shocks InterInter--Bank Deposit MarketsBank Deposit MarketsSyndicated LoansSyndicated Loans
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
Pillar IV:Banking Stability MeasuresBanking Stability Measures
43
Tail Risk Indicators: Mexico
Financial Stability Index:
Expected number of FIs in distress given that
Joint Probability of Distress (JPoD):Likelihood of common distress of all the FIs Expected number of FIs in distress given that
at least one became distressed (left scale). in the system (right scale).
JPOD-FSI: México
211. Lehman spillover, derivatives’ marketcrisis and mutual funds’ crisis (Oct- 0 0018
0.002
3 5
4
JPOD-FSI: MéxicoBSI JPODFSI JPOD
crisis and mutual funds crisis (Oct-2008).
2. H1N1 crisis (March-April-2009).
0.0012
0.0014
0.0016
0.0018
2.5
3
3.5
0.0006
0.0008
0.001
1.5
2
0
0.0002
0.0004
0
0.5
1
44
Tail Risk Indicators under Scenarios
45
Contagion Indicators: DiDe U.S.Distress Dependence Matrix: Probability that FI (row) falls in distress given that FI (column) falls in distress.
July 1 2007 September 12 2008July 1, 2007‐ September 12, 2008July 1, 2007 Citi BAC JPM Wacho WAMU GS LEH MER MS AIG Row
averageCitigroup 1.00 0.14 0.11 0.11 0.08 0.09 0.08 0.09 0.09 0.08 0.19Bank of America 0.12 1.00 0.27 0.27 0.11 0.11 0.10 0.12 0.12 0.15 0.24JPMorgan 0 15 0 42 1 00 0 31 0 13 0 19 0 16 0 19 0 18 0 17 0 29JPMorgan 0.15 0.42 1.00 0.31 0.13 0.19 0.16 0.19 0.18 0.17 0.29Wachovia 0.12 0.33 0.24 1.00 0.11 0.12 0.10 0.12 0.12 0.14 0.24Washington Mutual 0.16 0.28 0.21 0.23 1.00 0.12 0.12 0.16 0.13 0.15 0.26Goldman Sachs 0.17 0.25 0.28 0.21 0.11 1.00 0.31 0.28 0.31 0.17 0.31Lehman 0.22 0.32 0.32 0.26 0.15 0.43 1.00 0.35 0.33 0.20 0.36Merrill Lynch 0.19 0.32 0.33 0.25 0.17 0.33 0.31 1.00 0.31 0.20 0.34yMorgan Stanley 0.19 0.31 0.28 0.24 0.14 0.35 0.28 0.30 1.00 0.16 0.33AIG 0.07 0.14 0.10 0.10 0.05 0.07 0.06 0.07 0.06 1.00 0.17Column average 0.24 0.35 0.31 0.30 0.21 0.28 0.25 0.27 0.26 0.24 0.27
September 12, 2008 Citi BAC JPM Wacho WAMU GS LEH MER MS AIG Row average
Citigroup 1.00 0.20 0.19 0.14 0.07 0.17 0.13 0.14 0.16 0.11 0.23Bank of America 0.14 1.00 0.31 0.18 0.05 0.16 0.10 0.13 0.15 0.11 0.23JPMorgan 0.13 0.29 1.00 0.16 0.05 0.19 0.11 0.14 0.16 0.09 0.23W h i 0 34 0 60 0 55 1 00 0 17 0 36 0 27 0 31 0 34 0 29 0 42Wachovia 0.34 0.60 0.55 1.00 0.17 0.36 0.27 0.31 0.34 0.29 0.42Washington Mutual 0.93 0.97 0.95 0.94 1.00 0.91 0.88 0.92 0.91 0.89 0.93Goldman Sachs 0.15 0.19 0.24 0.13 0.06 1.00 0.18 0.20 0.27 0.11 0.25Lehman 0.47 0.53 0.58 0.43 0.25 0.75 1.00 0.59 0.62 0.37 0.56Merrill Lynch 0.32 0.41 0.47 0.30 0.16 0.53 0.37 1.00 0.48 0.26 0.43Morgan Stanley 0 21 0 28 0 29 0 19 0 09 0 40 0 22 0 27 1 00 0 14 0 31Morgan Stanley 0.21 0.28 0.29 0.19 0.09 0.40 0.22 0.27 1.00 0.14 0.31AIG 0.50 0.66 0.59 0.53 0.29 0.54 0.43 0.49 0.47 1.00 0.55Column average 0.42 0.51 0.52 0.40 0.22 0.50 0.37 0.42 0.46 0.34 0.41
46
Contagion Indicators: DiDe Mexico
47
Contagion Indicators: Toxicity Index
P(A)
Toxicity Index (TI): Toxicity of the distress of a country/FI on other countries/FIs.
P(A)
P(B) CIMDOMethodolog
P(A,B,C) JPoD
P(A B); P(A C); P(B C)P(C)Methodolog
yP(A,B); P(A,C); P(B,C)
Bayes’Bayes Law
)/()/()/()/()/()/()/()/()/(
CCPBCPACPCBPBBPABPCAPBAPAAP
For e.g. country (A)/FI(A):
)()()(
For e.g. country (A)/FI(A):
TI(A)=(P(B/A) + P(C/A))/n-1
48
Contagion Indicators:Toxicity IndexMexico
49
Contagion Indicators: Probability of Cascade Effects
Probability of Cascade Effects (PCE): Probability that at least one FI becomes distressed given that a given FI becomes distressed.
PCE Lehman/AIG (September 12).100
70
80
90LehmanAIG
50
60
70
30
40
0
10
20
7 7 7 7 7 7 7 7 7 7 7 7 8 8 8 8 8 8 8 8 8
1/1/
2007
2/1/
2007
3/1/
2007
4/1/
2007
5/1/
2007
6/1/
2007
7/1/
2007
8/1/
2007
9/1/
2007
10/1
/200
7
11/1
/200
7
12/1
/200
7
1/1/
2008
2/1/
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Contagion Indicators: Probability of Cascade EffectsMexico
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Pilar V: Contagion
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53
54
55
56
PoD Mexico & Mexican Banks
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PoDs: Mexico & Foreign Banks
58
DiDe Mexico/Sovereigns
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DiDe Mexican Subs/Parents
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Second-Round Effects
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Second-Round Effects
MacroeconomicFactors
FinancialFactors
Financial System´sFinancial System’sReturns
Comm BanksPoD
Dvlpmt BanksPoD
GSEsPoD
Pension FundsPoD
InsurancePoD
Mutual FundsPoD
Tail Risk (JPoD)Returns
MacroeconomicFactor
FinancialFactors
Comm BanksPoD
Dvlpmt BanksPoD
GSEsPoD
Pension FundsPoD
InsurancePoD
Mutual FundsPoD
ExposuresFinancial System´sMultivariate DensitySystemic
LGDs
ySystemicLoss Simulation
Financial System’sLoss Distribution
Financial Stability Measures
Marginal Contributionto Systemic Risk 62
Macro-financial Risk Zones
63
Macro-financial Risk Zones
Objective: Definition of “risk zones” based on the joint interaction ofmacroeconomic and financial indicators (JPoD).(Segoviano and Malik (2011) IMF WP forthcoming).( g ( ) g)
0.000
0.002 MSIAH(2)-VAR(1), 1999 (7) - 2009 (3)Jpodrev dlhouse
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009-0.002
0 5
1.0 Probabilities of Regime 1filtered predicted
smoothed
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
0.5
1.0 Probabilities of Regime 2filtered predicted
smoothed
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
0.5
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.
64
Thank You
65
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.