pillar iii presentation 11 18-14 - redacted version
TRANSCRIPT
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MARKET-BASED INDICATORS APPROACHTO STRESS TESTING: PRELIMINARY RESULTSBENJAMIN HUSTON
DALE GRAY
This presentation and its findings are intended as background for discussions with the U.S. stress testing experts in the context of the FSAP. Some findings have not undergone a full internal review and should not be shared outside the technical team involved in the US FSAP stress testing exercise.
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U.S FSAP PILLAR III:MARKET-BASED INDICATOR STRESS TESTING REGIME
Overview
Systemic Risk Dashboard
Contingent Claims Analysis (CCA) model, data, and historical outputs
CCA stress testing approach for Pillar III
Macro factor satellite model to project CCA risk indicators for scenarios
Network analysis
SyRin stress testing approach for Pillar III
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WHY MARKET-BASED INDICATORS?
Supervisory data is confidential and often cannot be utilized for FSAP stress testing purposes
Market prices contain valuable information that can be used to corroborate traditional stress testing methodologies and findings
Stress tests can be extended to sectors that are not traditionally subject to bank-like supervisory oversight
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SYSTEMIC RISK DASHBOARD
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SYSTEMIC RISK DASHBOARD (FORTHCOMING)
The market-indicator based stress tests will be prefaced by a “dashboard” which will use an established IMF framework to answer a series of questions to analysis systemic risk*
The dashboard will use an assortment of metrics to address key risks
Some of the metrics that will be featured in the dashboard include:
SRISK (Engle, 2010)
CoVaR (Andrian, 2008)
network/contagion analysis
SyRin:
Contingent Claims Analysis (CCA
*For further information see Systemic Risk Monitoring (‘SysMo’) Toolkit, IMF working paper No. 13168 5
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CONTINGENT CLAIMS ANALYSIS
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PREVIEW OF PROPOSED CCA APPROACH AND ITS BENEFITS
The CCA was used in the 2010 US FSAP (and in 9 other FSAPs)
The proposed approach for this US FSAP will cover more institutions and have broader coverage across the financial and corporate sectors than before
The analysis will be enhanced by integrating macro factor stress testing with measures of network interconnectedness
The outputs for base and adverse scenarios will include default probabilities, expected loss values, capital/asset ratios, fair value credit spreads, and capital shortfalls (i.e., the capital required to attain a “safe” credit risk level as measured by default probability and credit spreads)
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CORE CONCEPT: CONTINGENT CLAIMS ANALYSIS (CCA)
Assets = Equity + Risky Debt
= Equity + PV of Debt Payments – Expected Loss due to Default
= Implicit Call Option + PV of Debt Payments – Implicit Put Option
Assets
Equity or Jr Claims
Risky Debt
•Value of liabilities derived from value of assets
• Uncertainty in asset value
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DEFAULT PROCESS IN THE CCA STRUCTURAL MODEL
Valu
e of
Ass
ets
/ Lia
bilit
ies
Timet = 0 T = 1 year
Notional value of liabilities = Default Barrier
XT
Distribution of market value of assets
E[AT] = μ
Probability of Default ≈ EDF
Distance to default (DD) in σ
σ
Asset Volatility
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CALIBRATION AND DERIVED RISK INDICATORS
Market capitalization, equity volatility, and book values of debt are used to calculate implied value of assets and asset volatility. For each institution, these are used to calculate the:
(i) Probability of Default (one year PD)
(ii) Expected Losses (EL), Implicit Put Option
(iii) Implied credit spread called the “Fair-value CDS” (FVCDS) spread in basis points (= f(EL, t, risk-free rate))
(iv) The market implied government guarantee or contingent liability can be estimated from the difference between the (higher) FVCDS and the (lower) observed CDS spread (implicit guarantee lowers CDS)
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* Based on Credit Edge Data; see Annex 1 for details
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TRADEOFFS BETWEEN MARKET CAPITALIZATION, MARKET VALUEOF ASSETS AND DEFAULT PROBABILITY
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%.
A 0.5% EDF is near investment grade
0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
1,400,0001,500,0001,600,0001,700,0001,800,0001,900,0002,000,0002,100,000
Market Value of Assets (million $)
Mar
ket C
apita
lizat
ion
(mill
ion
$)
0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
0 5 10 15 20 25 30EDF, One Year Default Probability in Percent
Mar
ket C
apita
lizat
ion
(mill
ion
$)
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SAMPLE INSTITUTIONS
Number Selection CriteriaAsset Managers 41 10 billion USD plus market cap
NBFIs 13 10 billion USD plus market capInsurers 44 20 billion USD plus market cap
Corporates 32
Must be one of the largest non-financial DJIA public companies, or an auto maker that received government support, or an
iconic “new economy” technology company with a large and rapidly growing
market capUS Banks 46 20 billion USD plus market cap
GSEs 2 Must have entered government conservatorship
Non-US GSIBs 20 Must have been designated by the FSB as a GSIB and not be domiciled in the U.S.
Total 198
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PROPOSED APPROACH
Fit models using DFAST macro variables as regressors and default probabilities, expected losses, and fair-value CDS spreads as dependent variables
For capturing credit risk, use CreditEdge data from 2001 to present
For macro risk, use publicly available DFAST data
Conduct stress tests using three macro scenarios that coincide with those used in Pillar II and calculate impact on capital ratios and CCA credit risk indicators
Use a risk appetite factor, calibrated using the 2008-09 crisis, that is consistent with scenario adversity
Apply interconnectedness measures described in more detail in subsequent Network Analysis section 13
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HYPOTHETICAL EXAMPLE:CAPITAL/ASSET RATIO VS 1-YR PROBABILITY OF DEFAULT (PD)
y = 0.0734x‐0.483R² = 0.9339
0
0.05
0.1
0.15
0.2
0.25
0.3
0.00 0.50 1.00 1.50 2.00 2.50 3.00
Mar
ket
Cap
/Ass
ets
(%)
Probability of Default (%)
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HYPOTHETICAL EXAMPLE:USE OF SATELLITE MODEL TO PROJECT PD UNDER STRESS
Prob
abili
ty o
f Def
ault
(%)
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
9/1/
2014
12/1
/201
4
3/1/
2015
6/1/
2015
9/1/
2015
12/1
/201
5
3/1/
2016
6/1/
2016
9/1/
2016
12/1
/201
6
3/1/
2017
6/1/
2017
9/1/
2017
EDF Baseline
EDF Adverse 2
EDF Adverse 1
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HYPOTHETICAL EXAMPLE:PROJECTED CAPITAL / ASSET RATIO UNDER STRESS
*Dashed line is near-investment grade capital ratio threshold
Cap
ital/A
sset
s R
atio
(%)
0
0.05
0.1
0.15
0.2
0.25
9/1/20
14
1/1/20
15
5/1/20
15
9/1/20
15
1/1/20
16
5/1/20
16
9/1/20
16
1/1/20
17
5/1/20
17
9/1/20
17
Baseline
Adverse 2
Adverse 1
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HYPOTHETICAL EXAMPLE:5-YR CREDIT SPREADS UNDER STRESS
*Scenario Adverse 2 with higher market price of risk increases FVCDS spread
Basi
s po
ints
17
0
500
1000
1500
2000
2500
9/1/
2014
12/1
/201
4
3/1/
2015
6/1/
2015
9/1/
2015
12/1
/201
5
3/1/
2016
6/1/
2016
9/1/
2016
12/1
/201
6
3/1/
2017
6/1/
2017
9/1/
2017
FVCDS Baseline
FVCDS Adverse 2
FVCDS Adverse 1
Adverse 2 with higher MPR
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HYPOTHETICAL EXAMPLE:CAPITAL SHORTFALL UNDER STRESS
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
9/1/
2014
12/1
/201
4
3/1/
2015
6/1/
2015
9/1/
2015
12/1
/201
5
3/1/
2016
6/1/
2016
9/1/
2016
12/1
/201
6
3/1/
2017
6/1/
2017
9/1/
2017
Baseline
Adverse 1
Adverse 2
Cap
ital S
hort
fall
(%)
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MEASURING FINANCIAL SYSTEM PROBABILITIES OF DEFAULT
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MEASURING FINANCIAL SYSTEM EXPECTED LOSSES
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NETWORK ANALYSIS
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MOTIVATION FOR UTILIZING NETWORK INFORMATION
Prospectively use network connectivity statistics as an interaction term to inform our stress testing models (e.g., an entity or sector in-degree/out-degree variable combined with credit growth rates)
May yield better predictions of capital shortfalls in stressed scenarios
Network analysis can provide both a qualitative picture (graph) and quantitative measures of financial system dynamics over time
Capture potential domestic/international spill-over and contagion risks
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NETWORK EXAMPLES
*Illustrative examples of historical and scenario based Granger-Causality networks 23
Historical Network(2007 – 2013)
Base Scenario Adverse Scenario
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DERIVING THE NETWORKS
At the entity- and sector- levels:
1. Use algorithms to fit satellite models using DFAST macro variables as predictors and default probabilities and expected losses as dependent variables
2. Apply Granger-Causality tests to model residuals and derive adjacency matrices and networks
3. Describe networks in terms of topology (who is connected to who and to what extent), centrality (who is most important), and community structure (which parts of the network cluster together and share common features), and entropy (how much network “information” is there)
adjacency matrixresiduals network graph communities24
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THANK YOU!
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SYRINSYSTEMIC RISK AND INTERCONNECTEDNESS MEASURES
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SYRIN: SYSTEMIC RISK AND INTERCONNECTEDNESS MEASURES
Approach
See forthcoming IMF working paper for analytical details (Segoviano et al; 2014) and (Goodhart, Segoviano; 2006)
Derives widely-applicable financial stability indicators and system loss measures to detect direct/indirect linkages among institutions/sectors within a given financial system
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SYRIN
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ANNEX I:CONTINGENT CLAIMS ANALYSIS
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DIFFERENCE BETWEEN ACTUAL AND RISK-NEUTRAL DEFAULT PROBABILITY
Asset Value
Expected Asset
Distributions of Asset value at T
Drift of μ
Distress Barrier A0
T Time
“Actual “ Probability of Default
Drift of r
“Risk Adjusted “ Probability of Default
,A Mr SR
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MARKET PRICE OF RISK IN CCA/MKMV MODELSTo get the Risk-neutral Default Probability one must use the EDF and the Market Price of Risk
MKMV uses CAPM, the excess return of a security is equal to the beta of the security times the market risk premium.
Beta is equal to the correlation of the asset with the market times the volatility of the asset divided by the volatility of the market.
Here SR is the Sharpe Ratio for the market.
So, the market price of risk is:
( )Mr r
,cov( , )
var( )V M
A MM M
r rr
, ,( )M
A M A MM
rr SR
,A Mr SR
1,( )Risk Neutral A MktEDF N N EDF SR T
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FAIR VALUE CDS – FVCDS
Using Risk-Neutral EDF and the Loss Given Default (LGD for FVCDS is from the banking sector average LGD) FVCDS is Calculated
, c1 ln 1 *Risk Neutral Banking Se tor Ave Risk NeutralFVCDS LGD EDFT
Note that in designing scenarios, the market Sharp ratio can be changed to reflect the anticipated market price of risk for the particular scenario. For example a severe Adverse 3 scenario could be associated with a market Sharpe ratio similar to the level during after the Lehman crisis. Thus FVCDS and bank funding cost would increase reflecting the change in global risk appetite
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ANNEX II:NETWORK ANALYSIS
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GRANGER-CAUSALITY TESTS
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NETWORK TOPOLOGY: IN-DEGREE AND OUT-DEGREE
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CENTRALITY MEASURES: DEGREE CENTRALITY
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CENTRALITY MEASURES: EIGENVECTOR CENTRALITY
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SHANNON ENTROPY
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INFORMATION-CRITERIA BASED MODEL SELECTION AND BAYESIAN MODEL AVERAGING
Fit millions of models and select top 100with best information criteria scores
(below the red line)
Assess probability a top model is the“true model”(red line is cumulative 95% probability)
Assess how often specific variables appear in top models(those exceeding redline are likely inthe “true model”) 39
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ANNEX III:HISTORICAL CCA RISK INDICATORS
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CCA ASSESSMENT OF U.S. FINANCIAL SYSTEM RISKCRISIS PERIOD: MARCH 9, 2009
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CCA ASSESSMENT OF U.S. FINANCIAL SYSTEM RISK PRESENT PERIOD: SEPTEMBER 30, 2014
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CCA ASSESSMENT OF U.S. FINANCIAL SYSTEM RISKSPILLOVER THREATS: CRISIS (LEFT) AND PRESENT (RIGHT) PERIODS
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CCA ASSESSMENT OF U.S. FINANCIAL SYSTEM RISKSPILLOVER THREATS: CRISIS AND PRESENT PERIODS (CONTINUED)
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ANNEX IV:HISTORICAL EXAMPLE OF CCA RISK ZONE ANALYSIS
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CCA RISK ASSESSMENT EXAMPLE: CITIGROUP
CCA-based “risk-zones” can be used to assess an institution's level of credit risk from given market information
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ENTITY- AND SECTOR-LEVEL CCA ANALYSIS
CCA-indicators gave predictive warning of the Lehman collapse and the trouble at Citigroup. They can be utilized at both the entity and sector levels.
Mar
ket
Cap
/Ass
ets
(%)
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EXAMPLE: CITIGROUP (POST-LEHMAN)
From the time of the Lehman collapse until the time the financial crisis peak began to abate, Citigroup’s credit risk was well-captured by a concurrent CCA market-based indicator
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