PRMIA_January 2012_Alexis_HamarF

Measuring and managing systemic risks : An economic perspective of stressed dynamics

Alexis Hamar Moody’s Analytics

Agenda

2

1. Context & Motivations

2. Approaches for measuring systemic Risk

– Network Analysis- interconnectedness

– A Portfolio Model approach with market data

– Macro-Stress Testing/Scenario Analysis approach

3. Plausible/possible mitigation strategies

– Hedging Strategies

– Collateral Management Strategies

Context & Motivations 1

4

Context & Motivations

» Financial crisis has revealed that:

– Market, systems and Financial Institutions have reached an unprecented reach of

interconnectedness

– Lack of indicators and to some extent relevant data for measuring and monitoring systemic risks

– Therefore the importance to systemic risks and contagion effects

» Few questions to ponder :

– How much capital would a firm need if we have another financial crisis ?

– How might banks behave (raise lending standards, liquidity shortage,/hoard ,asset prices

decrease) ?

– How much the banking sector could lose if real estate values decline by 20% ?

– What impacts in the financial sector given a possible Sovereign Default ?

Systemic Risk and its contributors

» Can be defined as the risk that shocks affect a significant portion of the financial system

and triggers endogenous adverse situations.

» Credit Counterparty Risk

– CDS and wrong way risk

– Structured Credit Notes

» Contagion Effects:

– Size of interbanks exposures

– Liquidity Mistmatch

– Leverage

» Correlations

– Across firms and asset classes

– Contagion effects with exposure sizes and directions

Source BIS Paper 296

Approaches for measuring systemic risk 2

Super Nodes - interconnectedness

Source New York Times: Bill Marsh

Local/Regional Contagion

» Super Nodes:

– Nodes represent a

state, bank,

Corporate, SME,

Real Estate and

Retail ;

– Nodes link are

weighted based on

exposures

(liabilities)

– Each node actor has

a liquidity buffer and

allocated Tier 1

capital

Local Contagion Example

0

0,01

0,02

0,03

0,04

0,05

0,06

0,07

0,08

0,09

0,1

France Nation Muni Bank Large Corporate SME Real Estate Retail

Impact on French economy conditional to a Greek Default

Joint Default Correlation (Market based) Joint Default Correlation (Rating based)

Source Moody’s Analytics

Portfolio Model

» A portfolio model fed with market risk based data is useful for:

– Identifing and measuring common sources of risk;

– Modeling Conduits of Contagion through a unified correlation structure;

– Measuring Tail Risk effects (Expected Shortfall) and its risk contributors;

– Assessing Funding and Contigency Liquidity;

– Build/Design Collateral Management Strategies

– Hedge riskiest systemic contributors through Optimisation Strategies

Portfolio-Based Systemic Risk Measure

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

199701

199705

199709

199801

199805

199809

199901

199905

199909

200001

200005

200009

200101

200105

200109

200201

200205

200209

200301

200305

200309

200401

200405

200409

200501

200505

200509

200601

200605

200609

200701

200705

200709

200801

200805

200809

200901

200905

200909

201001

201005

201009

Portfolio Expected Shortfall Rate (target probability = 10 bps)

100 largest financial firms worldwide

Lehman Failure

Bank stress tests

Dot.com Crash

911 Attack

Asian Financial Crisis

Russian Crisis

Greece downgrade

Lehman Failure

Bank Stress Tests

Greece Downgrade

Bear Stearns Hedge Funds Liquidate


Financial System Has Become Increasingly Concentrated

12

0,4

0,45

0,5

0,55

0,6

0,65

1/1

/97

6/1

/97

11

/1/9

7

4/1

/98

9/1

/98

2/1

/99

7/1

/99

12

/1/9

9

5/1

/00

10

/1/0

0

3/1

/01

8/1

/01

1/1

/02

6/1

/02

11

/1/0

2

4/1

/03

9/1

/03

2/1

/04

7/1

/04

12

/1/0

4

5/1

/05

10

/1/0

5

3/1

/06

8/1

/06

1/1

/07

6/1

/07

11

/1/0

7

4/1

/08

9/1

/08

2/1

/09

7/1

/09

12

/1/0

9

5/1

/10

10

/1/1

0

Concentration Risk

For each monthly portfolio, we select 29 financial institutions with the highest TRCs (Tail

Risk Contribution), compute the sum of their TRCs, then obtain the ratio of the sum to the

corresponding portfolio capital.


Capturing G-SIFIs Systemic Risk

» Treat the 29 G-SIFIS as a portfolio, the portfolio’s tail risk (measures the contribution to the

risk of extreme loss) can be considered as the systemic risk measure as well as Expected

Shortfall

» Take the 29 G-SIFIS worldwide, and the sum of their liabilities is treated as a credit portfolio:

– Their reported liabilities as the exposure size balance sheet, with five year maturity.

– PIT EDF/SIEDF as PDs

– LGD = 100%

– Correlations from Moody’s Analytics Global Correlation Factor Model

– Time period: 1 December 2011

– Analysis performed using Moody’s Analytics Portfolio Model

Ranking G-SIFIS by their systemic risk

0

0,5

1

1,5

2

2,5

3

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Deu

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Dexia

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ea

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tree

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Unic

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it

Wells

Farg

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Tail Risk Contribution

Expected Shortfall


Forward looking systemic risk measurement and management: Stress Testing & Scenario Analysis

» Objective is to obtain a forward looking measure of systemic risk by simulating various

«states of the world » given transversal macro economic and financial shocks applied at

regional and sectorial level.

» Scenario Analysis based on PD and correlations (R-Squared) sensitivities:

– Forecast Scenario (up to March 2014)

» Correlated macro and financial Variables

– 42 financial and macro Variables

– 10 geographical zones (Australia & New Zealand, Central & South Africa, Eastern Europe & Russia, Japan,

Mexico & South America, Middle East & North Africa, South Asia, South East Asia, US & Canada, Western

Europe

» Combined with « break-up » Scenarios eg (Sovereign Default/Distress) and Major SIFIS

in distress:

» Greece

» Italy

» Ireland

Ranking G-SIFIS by their systemic risk (stressed vs non stressed)

-

0,50

1,00

1,50

2,00

2,50

3,00 D

exia

Ba

nq

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RB

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an

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CA

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ba

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No

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BP

CE

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ork

Me

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Expected Shortfall Rate Tail Risk Contribution

Stressed Expected Shortfall Rate Stressed Tail Risk Contribution


Stressed Sovereign R-Squareds

France

Spain

Germany

Italy

Ireland

Netherlands

Great Britain

Portugal

Belgium

Denmark

Norway

Sweden

Austria

United States

Brazil

China

Greece

Finland

Japan

Canada

Stressed RSQ

RSQ


SIFIS – Greece/Ireland/Italy Cross Asset Correlations

Credit_Suisse BPCE

Commerzbank

Goldman_Sachs

RBS

UBS

Barclays

BofA

BoChina

New_York_Mellon

BNP

Citi

Deutsche_Bank

CASA HSBC ING

JP_Morgan

Lloyds_TSB

Mitsubishi

Mizuho

Morgan_Stanley

Nordea

Santander

Socgen

State_Street

Sumitomo

Unicredit

Wells_Fargo

DexiaBanque

Asset Correlation Greece

Asset Correlation Italy

Asset Correlation Ireland


Plausible/Possible Mitigation strategies 3

Mitigating Systemic Risks ?

» Hedging Strategies ?

– Single Name Hedging

» CDS have proved to increase « by nature » systemic risk

» Wrong Way Risk

» CVA Hedging

– Portfolio Tail Risk Hegding - through Optimisation strategies under constraints:

» Economic (available/required budget)

» Risk (limits)

» Liquidity constraints

» Collateral Management Strategies ?

– Segregation of collateral in central repositories

– Haircut Valuation taking into consideration double default

» Central Clearing Counterparties ?

Collateral Management Strategies – Haircut Valuation

» Counterparty Credit Risk can be mitigated through dynamic collateralized strategies

whereby:

– The objective is to obtain a reliable and consistent measure of haircut level, as most often the link

between counterparty and collateral tend to be ignored.

» Haircut Valuation is used in the context of short term borrowing Strategy (stock lending

programs)

– Repos deals;

– ABCP;

» Stressed Haircuts bring value for pricing liquidity and assess liquidity mistmatched

– Balanche sheet resilience

– Funding in distressed markets

22

Collateral Management – Modeling Framework

» At inception, BANK “B” lends two bonds,“B1” and “B3”, to the counterparties Royal

Bank of Scotland and UBS, “C1” and “C2”

» BANK B in exchange receives as a collateral two bonds,“B2” and “B4”, plus an extra

amount set by the haircut level “ht”

» We model and price changes in the credit quality of the bonds, counterparties and

migration effects at a portfolio level during the life of the transaction, which are

incorporated into the haircut values: The borrowers are allowed to default and to

migrate to a different credit quality at any point during the life of the transaction

Business Case – Deal Structure

BANK B

UBS

RBS

Bond 1: XS0289185182

Bond 3: FR0010136382

Bond 2: XS0625359384

Bond 4: XS0231436238

Business Case – Haircuts

Counterparty Collateral ID

Baseline Scenario Stress Testing Scenario

(Counterparty -1 Notch)

Target Probability Target Probability

90% 95% 99% 90% 95% 99%

UBS Bond 2: XS0625359384 3% 6% 8% 6% 9% 14%

RBS Bond 4: XS0231436238 5% 9% 12% 8% 10% 15%

Counterparty Collateral ID

Baseline Scenario Stress Testing Scenario (Collateral

-1 Notch)

Target Probability Target Probability

90% 95% 99% 90% 95% 99%

UBS Bond 2: XS0625359384 3% 6% 8% 7% 11% 16%

RBS Bond 4: XS0231436238 5% 9% 12% 9% 12% 17%

Haircut on collateral assets (ECB source)

Credit Rating Haircut

Aaa to Aa3 3%

Aa3 to Baa3 18%

Below Baa3 Not Eligible Source Moody’s Analytics

Conclusions » Measures of systemic risks need to account for correlation in stressed conditions across firms (and

sovereigns) + contagion effects due to counteparty exposures.

» Unlike many other proposed approaches, the portfolio-based approach uses both market-based

inputs, such as EDF, SIEDF, and non-market-based inputs, e.g., “exposure size,” etc.

» Stressed conditions must be apply to risk parameters as well as to liquidity shocks for capturing actual

and predictive systemic situations.

» Collateral Management strategies bring insight for:

– Risk mitigation (price volatility and counterparty credit risk)

– Funding margining (eg margin calls) and market liquidity mismacthes

» A Redesign requires:

– We need to look at reliable data through model for simulation (stressing) contingent systemic situation;

– Macro – prudential oversight;

– Focus on sources of systemic risks

Documents

PRMIA_January 2012_Alexis_HamarF