Systemic Risk: From measurement to the New

Financial Stability Agenda

Jorge A. Chan-Lau

International Monetary Fund

The views expressed in this presentation do not necessarily reflect those of the IMF nor IMF policy

Séminaire Fonds Conrad-Leblanc,

FSA Université Laval

April 4, 2014

Measurement: (1) Statistical (2) Bottom-Up

Financial Stability:

(1) Regulation (2) Surveillance

Systemic Risk

Interconnectedness

SYSTEMIC RISK AND INTERCONNECTEDNESS

Systemic Risk: Definition

BIS, FSB, IMF (G20) defines systemic risk as:

Risk of disruption to financial services due to ..

• … full or partial impairment of financial system

• … with negative consequences to real economy

Systemic Risk and Interconnectedness

• Systemic risk arises from interconnectedness

• Interconnectedness arises from exposures

– Direct: interbank exposures, counterparty risk

– Indirect: contagion, common risk factors

• Systemic risk is dynamic

– Interconnectedness changes over time

Assets

Liabilities

Assets

Liabilities

Assets

Liabilities

Assets

Liabilities

Assets

Liabilities

Assets

Liabilities

Assets

Liabilities

Assets

Liabilities

Assets

Liabilities

Assets

Liabilities

Assets

Liabilities

Assets

Liabilities

Assets

Liabilities

Assets

Liabilities

Assets

Liabilities

Assets

Liabilities

Bank 1

Bank 2 Bank 1

Bank 1

Bank 1

Bank 2

Bank 2

Bank 2

Bank 3 Bank 3

Bank 3 Bank 3

Bank 4

Bank 4

Bank 4

Bank 4

Assets

Liabilities

Assets

Liabilities

Assets

Liabilities

Assets

Liabilities

Bank 2 Bank 1

Bank 3 Bank 4

Assets

Assets

Liabilities

Assets

Liabilities

Assets

Liabilities

Non-Bank 2 Non-Bank 1

Non-Bank 3

Non-Bank 4

Liabilities

Banking sector

Non-bank financial sector

Real Sector

Market and Risk Factors

SYSTEMIC RISK MEASUREMENT: STATISTICAL APPROACH

Statistical Approach

• Based on statistical approaches – Correlation analysis – Serial-correlation based measures of illiquidity – Financial Stress Indices – Tail dependence measures – Dynamic Conditional Correlation (DCC)

• Advantages – Simple – No extensive data collection – Theory-free

• Disadvantages – Potential structural changes are not captured – Cannot identify systemic institutions

0,2

0,3

0,4

0,5

0,6

0,7

0,8

Pearson

Spearman

Kendall

Correlation measures, vertical axis

0,4

0,5

0,6

0,7

0,8 Dynamic conditional correlation

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

5 percent

10 percent

Lower tail dependence

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

5 percent

10 percent

Upper tail dependence

Correlation Measures

0,0

0,2

0,4

0,6

0,8

1,0

0,0

0,5

1,0

1,5

2,0

2,5

3,0

3,5

Jun

-00

Jun

-01

Jun

-02

Jun

-03

Jun

-04

Jun

-05

Jun

-06

Jun

-07

Jun

-08

Jun

-09

Jun

-10

Jun

-11

Jun

-12

United States (left axis)

Euro Area (left axis)

3-year rolling correlation(right axis)

-10

-8

-6

-4

-2

0

2

4

6

8

10

0 1 2 3

Rea

l GD

P g

row

th, y

ear-

on

-yea

r ch

ange

in

per

cen

t

Financial stress index, horizontal axis (higher values correspond to higher stress)

United States

-6

-5

-4

-3

-2

-1

0

1

2

3

4

0 1 2 3

Rea

l GD

P g

row

th, y

ear-

on

-yea

r ch

ange

in

per

cen

t

Financial stress index, horizontal axis (higher values correspond to higher stress)

Euro area

Financial Stress Indices Quantile average: -3-month spread interbank rate to T-bill -Equity return, domestic stock index -Stock index volatility -Nominal exchange rate volatility -3-month T-bill volatility

SYSTEMIC RISK MEASUREMENT: BOTTOM-UP APPROACH OVERVIEW

Assets

Liabilities

Assets

Liabilities

Assets

Liabilities

Assets

Liabilities

Bank 1 Bank 2

Bank 3 Bank 4

Bottom-up Approach

What is the probability of default

(PD) of a bank ?

What happens to PD of other institutions if

peer fails?

Step 1

Market-based Methods

Fundamentals Methods

Step 2

Use Network or CoRisk Methods

Measure Systemic Risk with Credit

Portfolio Methods

Step 3

CALCULATING PROBABILITIES OF DEFAULT

Bottom-Up Approach

PD: Fundamental Methods

• Data

– Accounting data

– Firm-specific data

– Ratings data

– Economic data

• Models

– Linear discriminant analysis

– Duration and cohort analysis

– Most prudent estimation principle

– Econometric models

Fundamental Methods

PD: Market-Based Methods

• Infer PDs from credit risk-sensitive securities

• CDS spreads

• Bond yields

Protection buyer Protection seller

Payment contingent on default of reference issuer

CDS spread

Default

No default

PD

1-PD

RR

1

1 (1 )

1

r BPD

RR

B

PD: Market-Based Methods

• Asset swap spreads

InvestorBroker/Dealer Swap dealer

DefaultableBond

Borrow dirty price of defaultable bond

Pay Libor on dirty price of defaultable bond

Buy defaultable bond at dirty price

Receive coupon Cof defaultable bond

Pay coupon C of defaultable bond

Receive Libor plus asset swap spread on par value

of defaultable bond

PD: Market-Based Methods

• Equity price-based structural models

V = E + D

Asset value of the firm

V, B, E

D

E = Equity value

B = Debt value

V =Asset value of the firm

T

TDVp

)2/()/ln( 2

Equity price is price of call option

Risk-neutrality to real-world PDs

• Don’t forget market-based PDs are risk neutral

• Risk-neutral PDs overestimate real-world PDs

• Correct them with methods based on:

– Utility function

– CAPM

– Ratings

Risk neutral PD to Real-World PD ratio

0,0

0,4

0,8

1,2

1,6

2,0

0,0

2,0

4,0

6,0

8,0

10,0

12,0

14,0

16,0

18,0

Aaa Aa A Baa Ba B Caa andLower

Hull, Predescu andWhite (2005)

Berndt et al (2008)

(right axis)

IMPACT OF DEFAULT ON OTHER INSTITUTIONS

Bottom-Up Approach

Balance Sheet Network Analysis

Assets

Liabilities

Assets

Liabilities

Assets

Liabilities

Assets

Liabilities

Bank 1 Bank 2

Bank 3 Bank 4

A L

K

A L

K

A

K

A L

K

A L

K A

L

K

Loss of funding

Fire sale of assets

L

Credit Shock

Funding Shock

Credit losses

Balance Sheet Network Analysis

Bank 1

Bank 2

Bank N-1

Bank N

Bank 3

Bank J

Bank 1

Bank 2

Bank N-1

Bank N

Bank 3

Bank J

Bank 1

Bank 2

Bank N-1

Bank N

Bank 3

Bank J

PD =1

PD =1

PD =1

PD =1

PD based on final capital structure

Assets

Liabilities

Assets

Liabilities

Assets

Liabilities

Assets

Liabilities

Bank 1 Bank 2

Bank 3 Bank 4

IDEAL WORLD: data on exposures is available

CoRisk Analysis

REAL WORLD: missing data

Many complications: Reporting Mapping contracts into exposures Parents and Subsidiaries

CoRisk Analysis

WHAT TO DO??

HINT: Risk of connected firms move together

ANSWER: CORISK

Infer impact on PDs from risk comovements using econometric model

0

100

200

300

400

500

600

700Goldman Sachs

Citigroup

Barclays

5-year CDS spreads

CoRisk Analysis

PD of Firm A

PD of Firm B

Use quantile regression to find 90th quantile regression line

OLS regression or 50th quantile regression line

PD of interest

But higher response possible !

Median PD response

We can also find lower responses

High PD response

Use quantile regression to find 10th quantile regression line

Low PD response

HIGH RISK REGIME

NORMAL RISK REGIME

LOW RISK REGIME

CREDIT PORTFOLIO METHODS

Bottom-up Approach

Analogy with credit portfolio

Bank 1 Bank 2

Bank 3

Bank N-1 Bank N

Bank 4

Portfolio of risky “credits”

For each risky credit:

● Probability of default

● Exposure at default

● Loss given default

i.e. Guaranteed deposits

LOSSES

Systemic Risk of a Bank

Losses

Prob of Losses

Loss distribution with current PDs, EADs, and LGDs

VaR if Bank “J” is not distressed

VaR if Bank “J” is distressed

Loss distribution with PDs, EADs, and LGDs if Bank “J” is distressed

The difference between distressed VaR and the non-distressed VaR is the Systemic Risk of Bank J

One way to construct portfolio

Bank A Bank B

Bank D Bank E

Bank C

Bank A Bank B

Bank D Bank E

Bank C Bank J

Systemic risk = marginal contribution of Bank “J” to tail risk

Acharya et al, 2010; Tarashev et al 2010

Another way to construct portfolio

Bank A Bank B

Bank D Bank E

Bank C

Bank A Bank B

Bank D Bank E

Bank C Bank J

Systemic risk = Incremental contribution of Bank “J” to tail risk

Chan-Lau, 2010

Bank J Bank J

Bank A Bank B

Bank C

Bank D Bank E

FINANCIAL STABILITY: REGULATION

Assets

Liabilities

Assets

Liabilities

Assets

Liabilities

Assets

Liabilities

Bank 2 Bank 1

Bank 3 Bank 4

Three basic goals

Increase resilience of individual banks

Additional loss absorbency for systemic banks

Effective resolution of failed banks

BANK RESILIENCE

Financial Stability: Regulation

Bank resilience: capital buffers • Higher minimum capital ratios • Emphasize Tier-1 common equity • Phase out lower-quality capital by 2023 • Basel 3 minimum leverage ratio • Increased use of CoCos and bail-in-able subordinate debt

3,5 4 4 4,5 4,5 4,5 4,5

0,625 1,25 1,875 2,5

2013 2014 2015 2016 2017 2018 2019

Basel III phase-in arrangements, in percent of RWAs

Minimum common equity capital ratio

Capital conservation buffer

Liquidity death spiral

Reduced positions

Prices moving away from fundamentals

Funding Problems

Higher Margins

Losses on Existing Positions

Initial Losses e.g. credit

Bank resilience: liquidity buffers

• Raise liquidity coverage ratio (LCR)

• LCR = highly liquid assets to short-term obligations

• Consider run-offs and cash outflows under distress conditions

60 70

80 90

100

0

20

40

60

80

100

120

2013 2014 2015 2016 2017 2018 2019

Liquidity coverage ratio

LOSS ABSORBENCY

Financial Stability: Regulation

Globally Systemically Important Banks: Identification

Category (and weighting) Individual indicator Indicator weighting

Cross-jurisdictional activity (20%) Cross-jurisdictional claims 10%

Cross-jurisdictional liabilities 10%

Size (20%) Total exposures (as defined in Basel III leverage ratio) 20%

Interconnectedness (20%) Intra-financial system assets 6.67%

Intra-financial system liabilities 6.67%

Securities outstanding 6.67%

Substitutability/ financial institution infrastructure (20%)

Assets under custody 6.67%

Payments activity 6.67%

Underwritten transactions in debt and equity markets 6.67%

Complexity (20%) Notional amount of OTC derivatives 6.67%

Level 3 assets 6.67%

Trading and available-for-sale securities vulnerable to fire sale losses (excludes high quality liquid assets)

6.67%

Globally Sistemically Important Banks: Additional loss absorbency

130 – 229 bps

230 – 329 bps

530 – 629 bps

430 – 529 bps

330 – 429 bps

HSBC, JPMorgan

Barclays, BNP Paribas Citigroup, Deutsche Bank

BofA, Goldman Sachs, Credit Agricole, Mitsubishi UFJ, Morgan Stanley, RBS, UBS

B of China, B of NY, BBVA, BPCE, ICBC, ING, Mizuho, Nordea, Santander, Societe Generale, SC, State Street, Sumitomo, Unicredit, Wells Fargo

FINANCIAL STABILITY: SURVEILLANCE

IMF: Mandatory FSAPs

• FSAP = Financial Sector Assessment Program – IMF conducts in-depth examination of a country’s financial

sectors every 5 years (with WB in developing/EM country)

• FSSA = Financial Sector Stability Assessment – Conducted by IMF – Focused on financial stability

• FSSA – Soundness of financial institutions (stress tests,

interconnectedness analysis) – Quality of financial sector supervision – Safety net and crisis management preparedness

• FSSAs under the FSAP – Mandatory for 25 systemic jurisdictions – Extended to 29 jurisdictions

IMF FSAPs

Non-systemic

Systemic

IMF: Mandatory FSAPs Identifying Systemic Jurisdictions

• Emphasizes interconnectedness

• Broad coverage of possible cross-border transmission channels for shocks:

1. Banking claims

2. Debt portfolio holdings

3. Equity portfolio holdings

4. Price effects (“contagion”)

Four Global Financial Networks

Banking network: BIS Locational Statistics

Debt portfolio network: CPIS data

Price network: Equity return correlation,

MSCI price indices

Equity portfolio network: CPIS data

Links between jurisdictions weighted for:

• Size (PPP GDP)

• Complexity (derivatives claims vis-à-vis BIS reporting banks)

Unweighted

Weighted

Smaller links, with values below a threshold, are trimmed from the networks

Before trimming

After trimming

IMF: Mandatory FSAPs Identifying Systemic Jurisdictions

IMF: Mandatory FSAPs Constructing the networks

IMF: Mandatory FSAPs Constructing the networks

IMF: Mandatory FSAPs Constructing the networks

IMF: Mandatory FSAPs Constructing the networks

The Systemic Core: Clique Percolation Method (CPM)

k=6

k=5

CPM key parameters

Size k of k-clique

• Determines minimum size of group

Threshold

• Cutoff for trivial bilateral links

Optimal range determined by network properties

CPM key parameters

A systemic jurisdiction:

has many neighbors

who are also highly connected

Optimal k maximizes number of neighbors

Optimal k = [5,8] [4,8] Optimal threshold =

1.25 - 2.5 3.5 – 7.0

0.01 - 0.08

k* =5

Threshold* = 1.5 (exposures)

0.015 (correlations)

0

1

2

3

4

5

6

7

8

9

10

2 3 4 5 6 7 8 9

Bank network

Debt network

Equity network

Correlation network

k value

Average number of nearest neighbors

Optimal range for k 1/

1/ For the correlation network, the optimal range is [4,8]

AUT

DMK

FIN

HKG

IRE

NOR

POL RUS

SGP

KOR

SWE TUR

BEL JPN

NET ESP

CHE UK

FRA

DEU

ITA USA

LUX

BRA

CAN

IND

MEX

AUS Bank network

Price correlations network

CHN

IMF: Mandatory FSSAs Systemic 29 Jurisdictions

Australia (AUS) Austria (AUT) Belgium (BEL) Brazil (BRA) Canada (CAN) China (CHN) Denmark (DMK) Finland (FIN) France (FRA) Germany (DEU) Hong Kong (HKG) India (IND) Ireland (IRE) Italy (ITA) Japan (JPN) South Korea (KOR) Luxembourg (LUX) Mexico (MEX) Netherlands (NET)

Norway (NOR) Poland (POL) Russia (RUS) Singapore (SGP) Spain (ESP) Sweden (SWE) Switzerland (CHE) Turkey (TUR) United Kingdom (UK) United States (USA)

Caveats

• Non-reporting jurisdictions

• Shadow banking

• Data quality

Illustration of non-reporting

jurisdictions

Reporting countries

Non- reporting countries

Unreported Links

Cluster identified with reported data

Existing cluster cannot be identified with reported data

References

• Acharya et al, 2010, Measuring Systemic Risk, NYU working paper

• BCBS, 2013, Global Systemically Important Banks – Updated Assessment and the Higher Loss Absorbency Requirements, July.

• Bisias, D., M. Flood, A.W. Lo, and S. Valavanis, 2012, A Survey of Systemic Risk Analytics, Working Paper #0001, Office of Financial Research, U.S. Department of the Treasury

• Chan-Lau, J.A., 2009, The Globalization of Finance and its Implications for Financial Stability. Inernational Journal of Banking, Accounting and Finance 1, pp. 1 – 29

• Chan-Lau, J.A., 2013, Systemic Risk Assessment and Oversight (Risk Books)

• IMF, 2013, Mandatory Financial Stability Assessments Under the Financial Sector Assessment Program: Update

• IMF, 2012, Enhancing Surveillance: Interconnectedness and Clusters

• IMF, 2012, Enhancing Surveillance: Interconnectedness and Clusters, Background Material

• IMF, 2010, Integrating Stability Assessments Under the Financial Sector Assessment Program into Article IV Surveillance

• Jo, J.H., 2012, Managing Systemic Risk from the Perspective of the Financial Network Under Macroeconomic Distress, FSI Award 2012 Winning Paper, Financial Stability Institute, BIS

• Palla, G., I. Derenyi, I. Farkas, and T. Vicsek, 2005, Uncovering the Overlapping Community Structure of Complex Networks in Nature and Society, Nature 435, pp. 814 – 818.

• Tarashev et al, 2010, Attributing Systemic Risk to Individual Institutions, BIS Working Paper 318

QUESTIONS?