July 2009

Regulating Systemic Risk

(Abridged)

Viral Acharya, Lasse Heje Pedersen, Thomas Philippon, and Matthew Richardson

AQR Capital Management, LLC |Two Greenwich Plaza, Third Floor | Greenwich, CT 06830 |T: 203.742.3600 | F: 203.742.3100 |www.aqr.com

New York University Stern School of Business

Policy Proposal Part of NYU Stern Project and Follow-Up Research (Work in Progress)

Based on Chapter 13: “Regulating Systemic Risk”

2

Viral Acharya, Lasse Heje Pedersen, Thomas Philippon, and Matt Richardson

http://whitepapers.stern.nyu.edu/

Summary of our key findings

It is time to quantify systemic risk of financial institutions…

We propose a systemic risk measure: Marginal Expected Shortfall (MES)

• Average loss suffered by an institution when the market is in its left tail (say 5%)

Key findings based on MES:

• Securities dealers and brokers have been the most “systemic” institutions, every year, for past 45 years!

• MES of financials estimated during June 06-07 predicts their performance during ongoing crisisg p p g g g

• Top 4 out of 10 firms ranked by MES pre-crisis have effectively failed (Bear, Lehman, ML, CIT), tworeceived government support (GS, MS), others interesting (eTrade, CBRE, Charles Schwab, Ameritrade)

Systemic risk measures such as MES can be used to gauge which institutions are likely to suffer during Systemic risk measures such as MES can be used to gauge which institutions are likely to suffer duringaggregate crises (financial and/or economic) and potentially affect others

Systemic risk externalities can be regulated by “tax”ing institutions for their systemic risk contributions

We provide a scheme to calculate such systemic risk “tax” or the cost of insuring systemic risk of financials We provide a scheme to calculate such systemic risk tax or the cost of insuring systemic risk of financials

• Insurance cost based on June 2007 ranks at top Bear, Lehman, ML, MS, Fannie, Freddie, GS, Citigroupand JP Morgan

3

Systemic risk (MES5) over time for different groups

Annual Value-Weighted Marginal ES(5%) by groups.

0 11

0.09

0.11

0.05

0.07

0.01

0.03

-0.011963 1968 1973 1978 1983 1988 1993 1998 2003 2008

NBER Recession Depositories Others Insurance Sec. and Comm.

Ex Ante Systemic Risk (MES5) and Performance During Crisis

UBHCB K

.5c0

8

PB CT

BE RBRK

CB SSW FC

A TAGE

AFLCB

SA F

TRV

AOC

B RKCB H

B LKCG

B OT

M A

0ul

y07

to D

ec

TROW

BRK

S NVLUK

C IN F

LTRM TB

W FCM MC

JPMHUM

U NP

BK

N TRSTRV

PNCTM K PGR

USB

SEIC

UNMBBT

STT

SCHW

A LL

NYBA MTD

B LK

M ETAET

FIS

W LP AIZ

FNF

IC E

M A

W U N MX

U NH5ng c

risis

: Ju

C MA

E V

FITBRF

B EN

HB ANCNALNC

M IAX PBACC I

K EYLM

S TI

SEIC

M SC VHHNT

C OF

HIG

ZIONGS

JN S

W LP

P FGPRUCM E

AIZ

AM P NYX

U NH-.5R

etur

n du

ri

SOVW B

FNM

M ER

NCCCFCSLM

A IGBS C

CMB I

FREA BK LE HW M

HIG

E TFCA CAS CITGNW CBG

-1

0 .01 .02 .03 .04

5

MES5 measured June06 to Ju ne07

Ranking by systemic risk (MES) and cost of systemic insurance

MES (per share & $) Systemic Insurance Fee (% of equity & $) Ranking as of June 07 MES (%) MES ($) Ranking as of June 07 Fee (% Fee ($)E TRADE FINANCIAL CORP 1 37 BEAR STEARNS COMPANIES INC 1 6BEAR STEARNS COMPANIES 2 20 LEHMAN BROTHERS HOLDINGS INC 2 3LEHMAN BROTHERS HOLDINGS INC 2 3C B RICHARD ELLIS GROUP IN 3 54 MERRILL LYNCH & CO INC 3 2LEHMAN BROTHERS HOLDING 4 12 MORGAN STANLEY DEAN WITTER & 4 1MORGAN STANLEY DEAN WIT 5 4 COUNTRYWIDE FINANCIAL CORP 5 8GOLDMAN SACHS GROUP INC 6 5 FEDERAL HOME LOAN MORTGAGE C 6 4MERRILL LYNCH & CO INC 7 6 FEDERAL NATIONAL MORTGAGE AS 7 7SCHWAB CHARLES CORP NEW 8 16 GOLDMAN SACHS GROUP INC 8 5C I T GROUP INC NEW 9 50 E TRADE FINANCIAL CORP 9 15T D AMERITRADE HOLDING CO 10 42 C I T GROUP INC NEW 10 17T ROWE PRICE GROUP INC 11 36 AMERIPRISE FINANCIAL INC 11 14AMERIPRISE FINANCIAL INC 11 14EDWARDS A G INC 12 68 S L M CORP 12 20FEDERAL NATIONAL MORTGA 13 8 COMMERCE BANCORP INC NJ 13 21JANUS CAP GROUP INC 14 76 HARTFORD FINANCIAL SVCS GROUP 14 12FRANKLIN RESOURCES INC 15 13 METLIFE INC 15 11LEGG MASON INC 16 44 SOVEREIGN BANCORP INC 16 22AMERICAN CAPITAL STRATEG 17 62 UNUM GROUP 17 23STATE STREET CORP 18 24 WASHINGTON MUTUAL INC 18 16COUNTRYWIDE FINANCIAL CO 19 27 PRUDENTIAL FINANCIAL INC 19 13EATON VANCE CORP 20 75 JPMORGAN CHASE & CO 20 9

6

O C CO 0 5 JPMORGAN CHASE & CO 20 9

July 2009

Regulating Systemic Risk

(Unabridged)

Viral Acharya, Lasse Heje Pedersen, Thomas Philippon, and Matt Richardson

New York University Stern School of Business

AQR Capital Management, LLC |Two Greenwich Plaza, Third Floor | Greenwich, CT 06830 |T: 203.742.3600 | F: 203.742.3100 |www.aqr.com

New o U ve s ty Ste Sc oo o us ess

Overview: What to Do About Systemic Risk

Systemic risk: definition• The joint failure of a significant part of the financial institutions• Leading to the freezing of parts of the capital markets• That has the potential to disrupt the real economy

Systemic risk is very damaging• Losses of about 15 20% of GDP during banking crises over past 25 years• Losses of about 15-20% of GDP during banking crises over past 25 years.– Hoggarth, Ricardo and Saporta, “Costs of Banking System Instability: Some Empirical Evidence”, Journal of

Banking and Finance (2002)

• Systemic risk is different from risk: Lehman 08 vs. Barings 95

Key drivers:• Inability of private sector to resolve individual bank failures• Financial sector’s central role in the economy• Bailouts : Time-inconsistency problem

Moral hazard

• “Too big to fail” size bias; “Too interconnected to fail” counterparty risk bias;

8

• Too-big-to-fail size bias; Too-interconnected-to-fail counterparty risk bias;

“Too-many-to-fail” herding, systemic risk bias

Why Regulate Systemic Risk : Externalities

Spillover to the real economy, credit unavailability, payment system, etc.

• E.g., Acharya (2001, 2009), Diamond and Rajan (2005, 2009)

Market and funding liquidity spirals Market and funding liquidity spirals

• E.g., Geanakoplos (1997+), Brunnermeier and Pedersen (2009)

Fire sales and depressed prices can lead to allocation inefficiencies

• E.g., Acharya and Yorulmazer (2008)

What to do about systemic risk: treat it like pollution• Private regulation of systemic risk not feasible

St 0 A i t “ t i i k l t ”• Step 0: Appoint a “systemic risk regulator”– Central Bank a natural candidate (but issues of ensuring Central Bank independence must be addressed)– Macro-prudential regulation to supplement micro-prudential supervision by functional regulators

• Step 1: Measure systemic risk• Step 2: Require insurance against systemic risk, effectively taxing it and limiting it

Costs of not containing systemic risk: Without regulation there is

• Excessive leverage loading on aggregate risk concentration in illiquid assets

9

• Excessive leverage, loading on aggregate risk, concentration in illiquid assets

Measuring Systemic Risk

Key feature:

• For each bank: measure its contribution to a general crisis

Standard risk management calculation:

• Marginal Expected Shortfall (MES)

• Take 5% worst-case periods for the system (output, stocks, credit, financial sector,...)

• Ask: in those periods, how much did firm j contribute?

Analogy

• Allocation of economic risk capital within a firm– Each desk is charged for its (implicit) use of the firm’s economic capital

• Allocation of capital requirements within an economy– Government capital is a public goodp p g

Other measures:• ES: Expected shortfall of a firm based on own distribution

B t

10

• Beta

• Adrian and Brunnermeier (2008)’s CoVaR measure

A “demo” for the ongoing crisis

US financial institutions

• SIC code 6+

• Market capitalization > $5bln as of July 2007 (100 firms in total including all the usual suspects)Market capitalization > $5bln as of July 2007 (100 firms in total, including all the usual suspects)

Value-weighted CRSP Market returns

• MES based on 5%ile and 10%ile of Market returns

• Robust to employing financial sector as the “Market”

Pre-event period for systemic risk measurement: June 06 – June 07

Event period to explain realized performance: July 07 – Dec 08

Analysis of systemic risk measures (I-III for ongoing crisis; IV-VI for historical data)

I. Summary of measures and correlation

II. Predictive power of measures for realized systemic risk

III Cross sectional variation across types of institutions and bank characteristicsIII. Cross-sectional variation across types of institutions and bank characteristics

IV. Cyclical properties and stability of measures over time

V. Predictive power for crises

VI. Evidence on past crises

11

p

VII. Pricing of insurance against systemic risk contribution of individual institutions

I. Summary of measures and correlation (Table 1)

Panel APanel ADescriptive statistics of the measures Event return, beta, ES, MES5 and MES10.

Event

Return ES Beta MES5 MES10

Average -47% 2.73% 1.00 1.63% 1.26% Median -46% 2.52% 0.89 1.47% 1.17%Std. dev. 34% 0.92% 0.37 0.62% 0.48%

Min -100% 1.27% 0.34 0.39% 0.31% Max 36% 5.82% 2.10 3.36% 2.51%

Panel BPanel B

Sample correlation matrix of the measures Event return, beta, ES, MES5 and MES10

Event Return 1.00 ES -0.18 1.00

Beta -0.27 0.77 1.00 MES5 0 31 0 71 0 92 1 00MES5 -0.31 0.71 0.92 1.00MES10 -0.29 0.64 0.92 0.92 1.00

12

II. Predictive power for realized returns during the ongoing crisis

What works:

• Systemic risk measures– MES5

– MES10

– Beta

Wh d k ll What does not work as well:

• Firm-level risk measure– ES

In a horse race:

• MES5 and MES10 contain more relevant information than Beta

With what lead?

• Measures have predictive power up to 3-4 months lead time

13

Ex Ante Systemic Risk (MES5) and Performance During Crisis

UBHCB K

.5c0

8

Y = - 0.2 – 16.78 * MES5 + error, adj R2=8.70% (-2.24) (-3.26)

PB CT

BE RBRK

CB SSW FC

A TAGE

AFLCB

SA F

TRV

AOC

B RKCB H

B LKCG

B OT

M A

0ul

y07

to D

ec

TROW

BRK

S NVLUK

C IN F

LTRM TB

W FCM MC

JPMHUM

U NP

BK

N TRSTRV

PNCTM K PGR

USB

SEIC

UNMBBT

STT

SCHW

A LL

NYBA MTD

B LK

M ETAET

FIS

W LP AIZ

FNF

IC E

M A

W U N MX

U NH5ng c

risis

: Ju

C MA

E V

FITBRF

B EN

HB ANCNALNC

M IAX PBACC I

K EYLM

S TI

SEIC

M SC VHHNT

C OF

HIG

ZIONGS

JN S

W LP

P FGPRUCM E

AIZ

AM P NYX

U NH-.5R

etur

n du

ri

SOVW B

FNM

M ER

NCCCFCSLM

A IGBS C

CMB I

FREA BK LE HW M

HIG

E TFCA CAS CITGNW CBG

-1

0 .01 .02 .03 .04

14

MES5 measured June06 to Ju ne07

Ex Ante Systemic Risk (Beta) and Performance During Crisis

UBHCBK

.5c0

8

Y = -0.23 – 0.24 * beta + error, adj R2=6.19% (-2.45) (-2.77)

PBCT

B ERBRK

CBSSW FC

AT

UNP

AGEAFL

CB

SA F

TRV

AOC

BRKCBHBLK

CG

BOT

MA

0ul

y07

to D

ec

TROWSNV

LUK

CINF

LTRMTB

MMC

JP MHU M

UNP

B K

NTRSTRV

P NCTMKPGR

USB

SEIC

UNMB BT

STT

SC HW

A LL

NYBAM TD

BLK

META ET

FIS

W LP A IZ

FNF

ICE

MA

W UNM X

U NH5ing

cris

is: J

u

CMA

EV

FITBRF

B EN

HBA NCNALNC

MIA XPBAC CI

KEYLM

SLM

STI

MSCVHHNT

COF

HIG

ZIONGS

JNS

W LP

PFGPRU

CM E

A IZ

AMP NYX

U NH-.5R

etur

n du

r

SOV W B

FN M

MER

NCCCFCSLM

A IGBSC

CMBI

FREAB K LEHW M

HIG

ETFCACAS CITGNW C BG

-1

.5 1 1.5 2M k t B t d J 06 t J 07

15

Market Beta m easu red June06 to June07

Ex Ante Firm-level Risk (ES) and Performance During Crisis

UBHCBK

.5c0

8

Y = -0.29 – 6.73 * ES + error, adj R2=2.44% (-2.80) (-1.88)

PBCT

B ERBR K

CBSSW FC

ATAGE

AFLCB

S AF

TRV

AOC

B RKCBH

BLKCG

BOT

MA

0ul

y07

to D

ec

TR OW

BR K

SN VL UK

CINF

LTR MTB

W FCMMC

J PMHU M

UNP

BK

N TRSTRV

PNCTMK PGR

U SB

SEIC

UNMBBT

STT

SC HW

A LL

NYBAMTD

BLK

MET A ET

FIS

W LPA IZ

FNF

ICE

MA

W U NMX

UNH5ng c

risis

: Ju

CMA

EV

FITBRF

BEN

HBA NCNALN C

MIAXPBAC CIKEY

LM

STI

SEIC

MSCVHHNT

COF

HIG

ZIONGS

JNS

W LP

P FGPRU

CME

A IZ

AMP N YX

UNH-.5R

etur

n du

ri

SOVW B

FNM

ME R

NCCCFCSLM

AIGB SC

CMBI

FRE

HNT

AB K LEHW M

HIG

ETFCACAS CITGNW CBG

-1

.01 .02 .03 .04 .05 .06

16

ES measured June06 to June07

Ranking by systemic risk (MES) as of June 2007

MES (per share & $)

• Top 4 out of 10 firms ranked by MES pre-crisis have effectively failed (Bear, Lehman, ML, CIT), two received government support (GS, MS), others interesting (eTrade, CBRE, Charles Schwab, Ameritrade)• On a dollar basis for MES or insurance costs, Citigroup and JPMorgan also enter top 10

Ranking as of June 07 MES (%) MES ($)

E TRADE FINANCIAL CORP 1 37

BEAR STEARNS COMPANIES INC 2 20

C B RICHARD ELLIS GROUP INC 3 54

LEHMAN BROTHERS HOLDINGS INC 4 12LEHMAN BROTHERS HOLDINGS INC 4 12

MORGAN STANLEY DEAN WITTER & CO 5 4

GOLDMAN SACHS GROUP INC 6 5

MERRILL LYNCH & CO INC 7 6

SCHWAB CHARLES CORP NEW 8 16

C I T GROUP INC NEW 9 50

T D AMERITRADE HOLDING CORP 10 42

T ROWE PRICE GROUP INC 11 36

EDWARDS A G INC 12 68

FEDERAL NATIONAL MORTGAGE ASSN 13 8

JANUS CAP GROUP INC 14 76

FRANKLIN RESOURCES INC 15 13

LEGG MASON INC 16 44

AMERICAN CAPITAL STRATEGIES LTD 17 62

STATE STREET CORP 18 24

COUNTRYWIDE FINANCIAL CORP 19 27

17

COUNTRYWIDE FINANCIAL CORP 19 27

EATON VANCE CORP 20 75

II. Predictive power: Horse-race (Table 2)

Panel A

The dependent variable is Event return, the company stock returns during the crisis

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6Intercept -0.29***

(-2.80) -0.23** (-2.45)

-0.20** (-2.24)

-0.22** (-2.43)

-0.23** (-2.22)

-0.22** (-2.05)

ES -6.73* (-1.88)

2.03 (0.37)

-0.29 (-0.05)

Beta -0.24*** (-2.77)

0.08 (0.32)

0.04 (0.13)

MES5 16 78*** 23 27*MES5 -16.78(-3.26)

-23.27(-1.73)

MES10 -20.42*** (-3.08)

-22.74 (-1.25)

Adj. R2 2.44% 6.19% 8.70% 7.75% 7.22% 5.88%

18

Predictive power: Lead (Table 2)

Panel B

The dependent variable is Event return, the company stock returns during the crisis. The explanatory variable is MES5 as computed over different pre-crisis periods.

June06 June06 May06 Apr06 Mar06 Jan06 June06-June07

June06-May07

May06-Apr07

Apr06-Mar07

Mar06-Feb07

Jan06-Dec06

Intercept -0.20**

(-2.24)-0.26*** (-3.17)

-0.32*** (-3.86)

-0.32*** (-3.86)

-0.39*** (-5.41)

-0.38*** (-5.48)

MES5 -16.78*** (-3.26)

-13.92*** (-2.92)

-8.62* (-1.97)

-8.62* (-1.97)

-5.43 (-1.27)

-6.55 (-1.46)( 3.26) ( 2.92) ( 1.97) ( 1.97) ( 1.27) ( 1.46)

Adj. R2 8.70% 6.92% 2.76% 2.76% 0.60% 1.12%

Panel C The dependent variable is Event return, the company stock returns during the crisis. The explanatory variable is MES10 as computed over different pre-crisis periods.

June06-J 07

June06-M 07

May06-A 07

Apr06-M 07

Mar06-F b07

Jan06-D 06June07 May07 Apr07 Mar07 Feb07 Dec06

Intercept -0.22**

(-2.43)-0.24*** (-2.91)

-0.30*** (-3.63)

-0.30*** (-3.56)

-0.35*** (-4.39)

-0.39*** (-5.72)

MES10 -20.42*** (-3.08)

-18.70*** (-2.97)

-13.47** (-2.26)

-13.30** (-2.30)

-10.24 (-1.65)

-7.67 (-1.45)

Adj R2 7 75% 7 18% 3 90% 4 07% 1 67% 1 08%

19

Adj. R 7.75% 7.18% 3.90% 4.07% 1.67% 1.08%

III. Cross-sectional variation across types of financial institutions

Based on two-digit SIC codes:

• 60 = Depository Institutions (JPMorgan, Citigroup, WAMU,…)

• 61 = Non-depository Institutions (Fannie Freddie AMEX Mastercard )61 Non depository Institutions (Fannie, Freddie, AMEX, Mastercard,…)

+ 62 other than 6211 (CBOT, CME, etc.)

+ 65 = Real estate

+ 67 = Holding and Other Investment Offices (Fifth Third, NYSE Euronext, Blackrock, …)

• 63 = Insurance Carriers (AIG, Berkshire Hathaway, Countrywide,…)

+ 64 = Insurance Agents, Brokers, Service (Metlife, Hartford Financial, …)

• 6211 = Security and Commodity Brokers (Goldman Sachs, Morgan Stanley,…)

Summary of results:

• Across categories, Security and Commodity Brokers have the highest systemic risk

• Within institution type some evidence that systemic risk is higher for (results not yet reported)• Within institution type, some evidence that systemic risk is higher for (results not yet reported)- Larger size

– Lower capital to assets ratio

– Higher debt to assets ratio

20

– Higher short-term debt to assets ratio (SIC code 6211 = Security brokers and dealers, 6221 = Commodity contractsbrokers and dealers)

Systemic risk of different types of institutions (Table 3)

Panel A

Descriptive statistics of the measures Event return, beta, ES, MES5 and MES10.

Depository Institutions, SIC code=60. EventReturn ES Beta MES5 MES10

Average -43.16% 2.23% 0.87 1.42% 1.12% Median -41.07% 2.11% 0.82 1.31% 1.09%

Std. Dev. 35.40% 0.48% 0.19 0.34% 0.25% Min -99 61% 1 27% 0 53 0 88% 0 66%Min 99.61% 1.27% 0.53 0.88% 0.66%Max 35.63% 3.58% 1.33 2.12% 1.71%

N 29 29 29 29 29

Panel B Descriptive statistics of the measures Event return, beta, ES, MES5 and MES10.

Other: Non-depository Institutions etc. SIC code=61, 62(except 6211), 65, 67. EventReturn ES Beta MES5 MES10

Average -52.29% 3.35% 1.22 1.92% 1.48% Median -57.90% 3.17% 1.18 1.83% 1.46%

Std. Dev. 32.26% 1.06% 0.35 0.63% 0.51%Min -98.78% 1.79% 0.67 0.92% 0.31% Max 10.12% 5.82% 2.10 3.36% 2.40%

N 27 27 27 27 27

21

Systemic risk of different types of institutions (Table 3)

Panel C

Descriptive statistics of the measures Event return, beta, ES, MES5 and MES10.

Insurance: SIC code=63 and 64. EventReturn ES Beta MES5 MES10

Average -43.78% 2.44% 0.78 1.28% 0.98% Median -43.84% 2.29% 0.76 1.38% 0.98%

Std. Dev. 32.26% 0.69% 0.23 0.39% 0.30% Min -98.47% 1.39% 0.34 0.39% 0.36% Max 13 56% 4 42% 1 51 2 09% 1 67%Max 13.56% 4.42% 1.51 2.09% 1.67%

N 36 36 36 36 36

Panel D Descriptive statistics of the measures Event return, beta, ES, MES5 and MES10.

Security and Commodity Brokers: SIC code=6211. EventReturn ES Beta MES5 MES10

Average -59.09% 3.61% 1.61 2.68% 2.04% Median -68.40% 3.46% 1.60 2.64% 2.10%

Std. Dev. 36.23% 0.68% 0.24 0.34% 0.35%Min -99.82% 2.88% 1.21 2.26% 1.38% Max -0.71% 5.24% 1.96 3.29% 2.51%

N 10 10 10 10 10

22

Effect of institution type on systemic risk (Table 4)

Fixed Effects Realized

return in the crisis

MES5 Beta

Depository Institutions

-0.43***(-6.93)

0.01***(17.07)

0.87***(18.12)

Other -0.52*** (-8.10)

0.02*** (22.17)

1.22*** (24.66)

Insurance -0.44*** (-7.83)

0.01*** (17.11)

0.78*** (18.31)

(

Security and Commodity

Brokers

-0.59*** (-5.57)

0.03*** (18.88)

1.61*** (19.76)

Adj. R2 66.42% 93.33% 94.21%

In all columns, all coefficients save for the first and third are statistically significantly different from each other.

Pre-crisis systemic risk contribution (MES5 * Size)

Expected Shortfall (in Billion Dollars) Data throughJune 07

0 1 2 3 4 50 1 2 3 4 5

CJPMBACMSGSGS

MERWFCFNMAIGWBAXPLEHBENMETWMSCHPRU

BKBKNYXFREBSCUSBUNHUNP

24

UNPSTT

NOTE: Systemic risk (MES) is NOT just Size!

ICE

.04

FNMAMTD

GS

JNSCIT

CBGAMP

ICE

NYX

TROW

MER

AGE

BSC

MSSCHW

LEH

ETFC

NMX

.03

SOV

PBCT SNV UB

CMA

RFMTB

WBWFCHBAN

JPMBK

MI

NCC

NTRS

BACPNCKEY

C

BBT

STT

WM

CBHHCBK

WU

LUK

CBSS

EV

FITB

BEN

UNP

FNM

AXP

LMSEIC

FRECOF

ACAS

BLK

JNS

FISCME

BOT

MA

BER

CINF

LTR

CNAHUM

LNCCBSAF

TRVAOCTMK

CIPGR

CFC

UNM

MBI

ABK HIG METAET

PFG

PRUCG

AIZ

GNW

TROWAGE

.02

ME

S5

PBCT SNV MI

USBSTI

NYBZION

CBSSATSLM

BRK

CINF

MMC AFL

AOCTMK

AIG

CVHHNT ALL

BRK

WLP

AIZFNF

UNH

.01

0

6 8 10 12 14Log Total Assets

Y = 0.02 - 0.0003 * log AT + error, adj R2 = -0.32% (5 08) ( 82)

25

(5.08) (-.82)

IV. Cyclical properties and stability over time

Time-series analysis:

Systemic risk measures rise as crises approach a crucial feature for our proposal to follow Systemic risk measures rise as crises approach, a crucial feature for our proposal to follow

Beta: Appears more variable over time; Picks up many recessions; Not always coincident with market stresses

MES: Appears more stable; Picks up recessions as well as market stresses (e.g., 1987 stock market crash)

Important observation: Systemic risk of financial sector and recessions are NOT always coincident

Systemic risk of Security Dealers and Brokers is robustly higher than that of others over 1963 to 2008

Likely a reflection of their (tail) risk-taking, higher leverage and financial fragility (short-term debt)

However, during stress periods, the gap in systemic risk between different groups appears to narrow

26

Systemic risk (MES5) over time for different groups

Annual Value-Weighted Marginal ES(5%) by groups.

0 11

0.09

0.11

0.05

0.07

0.01

0.03

-0.011963 1968 1973 1978 1983 1988 1993 1998 2003 2008

NBER Recession Depositories Others Insurance Sec. and Comm.

Systemic risk (MES5) over time for different groups

Annual Equally-Weighted Marginal ES(5%) by groups.

0 075

0.095

0.055

0.075

0.015

0.035

-0.0051963 1968 1973 1978 1983 1988 1993 1998 2003 2008

NBER Recession Depositories Others Insurance Sec. and Comm.

Systemic risk (beta) over time for different groups

Annual Value-Weighted Beta wrt. Market return by groups.

2 2

2.7

1.7

2.2

0.7

1.2

0.21963 1968 1973 1978 1983 1988 1993 1998 2003 2008

NBER Recession Depositories Others Insurance Sec and CommNBER Recession Depositories Others Insurance Sec. and Comm.

Systemic risk (beta) over time for different groups

 Annual Equally-Weighted Beta wrt. Market return by groups.

3

2.5

3

1.5

2

0.5

1

01963 1968 1973 1978 1983 1988 1993 1998 2003 2008

NBER Recession Depositories Others Insurance Sec and CommNBER Recession Depositories Others Insurance Sec. and Comm.

Firm-level risk (ES) over time for different groups

 Annual Value-Weighted ES(5%) by groups.

0.11

0.13

0.15

0.07

0.09

0.03

0.05

-0.01

0.01

1963 1968 1973 1978 1983 1988 1993 1998 2003 2008

NBER Recession Depositories Others Insurance Sec. and Comm.

Firm-level risk (ES) over time for different groups

 Annual Equally-Weighted ES(5%) by groups.

0.11

0.13

0.15

0.07

0.09

0.03

0.05

-0.01

0.01

1963 1968 1973 1978 1983 1988 1993 1998 2003 2008

NBER Recession Depositories Others Insurance Sec. and Comm.

V. Predictive power of systemic risk measures for crises (Table 5)

MES f th fi i l t di t th li d h tf ll f th k t ith l dMES of the financial sector predicts the realized shortfall of the market with a one-year lead:

Dependent Variable is expected shortfall of the market at 5%

Independent variable Equally weighted market beta Value weighted market betap q y g gIntercept 0.017***

(5.39) 0.0162***

(4.81) 0.014***

(4.16) 0.001 (0.13)

-0.001 (-0.10)

-0.010 (-1.13)

Lag 1 0.005 (1.15)

0.0004 (0.06)

-0.002 (-0.29)

0.02141*** (2.99)

0.017* (1.90)

0.022** (2.33)

Lag 2 0.006 (0.91)

-0.002 (-0.33)

0.006 (0.63)

-0.008 (-0.71)

Lag 3 0.013** (2.08)

0.019** (2.04)

Adj. R2 0.40% 0.28% 4.39% 8.95% 7.75% 11.75%

Independent variable Equally weighted MES5 Value weighted MES5 Intercept 0.014***

(6.61) 0.013***

(5.86) 0.014***

(6.61) 0.013***

(5.86) 0.014***

(6.61) 0.013***

(5.86) Lag 1 0.357***

(3.03) 0.327***

(2.69) 0.357***

(3.03) 0.327***

(2.69) 0.357***

(3.03) 0.327***

(2.69) Lag 2 0 097 0 031 0 097 0 031 0 097 0 031Lag 2 0.097

(0.82) 0.031(0.24)

0.097(0.82)

0.031 (0.24)

0.097(0.82)

0.031(0.24)

Lag 3 0.151 (1.24)

0.151 (1.24)

0.151 (1.24)

Adj. R2 16.48% 17.05% 16.48% 17.05% 16.48% 17.05%

33

VI. Evidence for other crises (Details available upon request)

Early 80’s- Pre-event = 81-82; Event = 83-84

- MES works better than Beta

Savings and Loans- Pre-event = 87; Event = 88-89

N ith MES B t d d j b- Neither MES nor Beta do a good job

Late 90’s- Pre-event = Oct 96 to Sep 97; Event = Oct 97 till end of 98

- Beta does better than MES

In ALL cases, ES does the worst compared to systemic or systematic risk measures

34

Advantages of MES and caveats

Does not rely on a particular “percentile” (it looks at the whole tail)

• This is especially helpful when one needs to condition on a tail event

• Calculation straightforwardCalculation straightforward

Flexible technology

• Can be done for profits, credit losses, etc.

• Break down by divisions, desks, assets, geographical regions

• Consistent with M&As, changes in size, positions, etc.

Caveats on statistical methods

• Cyclical behavior: Can capture systemic risk, but may be just systematic risk (beta)

• Past data vs. future crisis

Complement with scenario analysis

Qualitative inputs: Inter-connectedness, complexity, concentration (e.g., JPMorgan Chase)

35

Q p , p y, ( g , g )

VII. Our proposal for regulating systemic risk

1. Systemic Capital Requirement (Basel III)

• Capital requirement proportional to estimated systemic risk

2. Systemic Fees (FDIC-style)

• Fees proportional to estimated systemic risk

• Create systemic fund.

3. Our preferred approach: Systemic Insurance provided by the private/publicp pp y p y p p

• Compulsory insurance of each bank’s own losses during general crisis

• Payment goes to systemic fund, not the bank itself

M k i f i b f h i b h f h• Market price of insurance, but most of the insurance bought from the government

– Not enough capital for provision of ALL systemic insurance (Problems with Monolines, A.I.G.,…)

– Analogy to terrorism reinsurance by the government (TRIA, 2002)

Advantages of our proposal

• Incentives to “organically” limit systemic risk (e.g., to lower short-term debt, correlation with market)

• Estimates of systemic risk (by regulator and by the insurance market)

36

• Reduce risk and cost of bailout (systemic fund)

Specific design questions

What should be the maturity at the time of each purchase of insurance?

• 5-year (should roughly cover expected time between cycles)

How frequently should the insurance be acquired?

• Monthly to reflect changing risk in new insurance purchase, so rolling 1/60th of insurance purchased each month

Should the price charged by the regulator be the same as that charged by the private sector?

• Perhaps the same, but there could be some discounting as funds when deployed for resolution also earn a return

How should the private insurers’ counterparty risk be mitigated?

• Well-capitalized; Perhaps even hold 100% capital against the largest insurance they have provided; And perhapseven trade on centralized clearinghouse/exchange…

• Buyers of insurance will have to pay for capitalization of these insurance providers

What proportion of insurance provision should be from the private sector?

• Need well-capitalized but also competitive insurance provision

When do institutions “fail”? How should the regulator deploy the insurance premia and payments collected?

• Pre-announced early intervention points (as in FDIC’s Prompt Corrective Action) for not requiring any furtherinsurance purchase, suspension of dividend payments, receivership, …

37

p , p p y , p,

• Augment the systemic risk fund; Use reserves for resolution of systemic failures (guarantees, bailouts, etc.)

Regulating systemic risk: Pros and cons of various approaches

C it l T P i t I P bli /P i tCapital requirements

Taxes Private Insurance Public/Private Insurance

Advantages Consistent with existing regulations

Transparent and easy to implement

Easy to adjustCreate a systemic

fund

No need for extra capital on BS

Extract market prices

Market pricePublic power

Disadvantages Cost of keeping large capital on balance sheet

Hard to figure out the price

Market not large enough for real systemic risk

LOLR still there

GovernanceCoordination

Find correct public price

38

Illustration of how systemic risk insurance might be priced

G l Goals

• Illustrate that our proposal is not just an abstract notion but can be readily implemented

• To provide a rough estimate of the price of systemic risk insurance and study its determinants

Assumptions: Multi-variate normality, representative agent, constant relative risk-aversion (CRRA)

• Based on Stapleton and Subrahmanyam (1984)

Additional calculations (To be completed)

• Assess the impact of buying systemic risk insurance on earnings and P/E ratios of banks

• Construct and understand time-series of insurance costs for systemically important institutions

• Understand the relative costs across institutions

NOTE: Under market pricing of insurance

• Actual prices may not be based on multi normal distributions• Actual prices may not be based on multi-normal distributions

• Market price will factor in non-normal distributions, conditionality of distributions in market tail events,time-varying risk premium, etc.– Key benefit of private part of public-private scheme

39

– Analogy: Black-Scholes model versus Market prices of options

Outline

Th ti l l l ti Theoretical calculation

• Specification of the systemic risk insurance

• Valuation

Illustrative calculations

• Base case parameters

• 3D graphs of the effect of volatility and correlation parameters

Calculations of financial firm insurance charges

• Description of the assumptions and model runs

• Figures presenting most systemic firms, 2004-2007

40

Specification of the systemic risk insurance

Assume that the financial institution is required to take out insurance on systemic losses tied to the marketvalue of equity of the firm and the overall sector.

A systemic risk insurance is defined by:

• the market value of the equity of the aggregate financial sector falling below a threshold, and

• the required payment at maturity of the claim, which is the difference between some pre-specified marketvalue of the equity of the financial institution and its actual market value.

The payoff at maturity T can be represented mathematically as The payoff at maturity T can be represented mathematically as

max( ,0) max( ,0)S MTM

iS MTM

K SS iTK S K S

Question: What is its price assuming joint log-normality of the two equity returns?

Illustrative calculation:

• Market volatility of 20%, bank volatility of 50% and correlation of 50%

• The price of ensuring bank value beyond 50% loss when market has made 50% loss is of the order of$5.5bln on a $1trn notional [Black-Scholes on bank = $66bln, Black-Scholes on market = $1.5bln]

• Price can however rise dramatically when there is a “perfect storm”

41

Price can however rise dramatically when there is a perfect storm

Solution to the valuation problem

)0max()0,max(1 SK dSdSSSSKV MTMS

)(

, )0,max(

1

00KK

iTMTiTMTiTSSKrt

dSdSSSSK

dSdSSSSKV

iSMS

iMTMStT

,,)(00

iTMTiTMTiTSrdSdSSSSK

itT

Where,

SrtTSSrtTS

SStTiTMT

SitTitiT

MStTMtMT

TMi

iTMTMiiSMSeSS

2

2)(2

2)(

)21(21

2lnln)(ln

2lnln)(ln

)1()(21,

tT

SrtTS

tT

SrtTS

Mi

tT

StS

tTT

SitTitiT

MStTMtMT

iS

itiT

MS

MtMT

2

2)(2

2)(

22

lnln)(lnlnln)(ln

)(

2

tTtTMiiSMS

Base Case Parameters

Initial bank asset value: 10itS Initial market asset value:

Bank crisis boundary:

it10MtS

5iSK

Market crisis boundary:

Time-to-maturity:5

MSK

4 tT Risk-free rate:

Bank volatility: %50iS

%4r

Market volatility:

Correlation:

%20MS

5.0Mi

Joint effects of correlation and bank volatility

Joint effects of correlation and market volatility

Joint effects of bank and market volatility

Description of Financial Firm Insurance Charges

A ti Assumptions

• Multivariate normality– Each year, 2004-2007, take the prior year’s volatility and correlation of the equity of the financial firms and the

market.

• Payoff based on (i) an event trigger of a 40% drop in the stock market, and (ii) the difference between astrike value of equity such that (market value of equity/total assets)=10% and the current equity value ofthe firm. (We also tried 25% drop in the market, and strikes based on 5% and 2.5%.)

• 4-year maturity and current 1-year treasury rate.

Caveats

• Strong evidence that multivariate normality does not hold, in particular, equity returns have fat tails andare more correlated during crises. This would greatly increase the insurance cost though not necessarilychange the ranking across firms.

• We treat the liabilities of insurance companies, investment banks and commercial banks on the samelevel in terms of measuring the insurance payoff. It is desirable to perhaps treat different types offinancial institutions differently.

Illustrative calculations (40% drop, 10% equity/assets)us a ve ca cu a o s ( 0% d op, 0% equ y/asse s)

• Time-series of insurance charges of top 10 systemic firms (based on 2007) over the 2004-2007 period.

• Tables of systemic firm ranking based on insurance charges over the period 2004-2007 as a function of $charges and of $ charges as a % of equity value.

47

Illustrative insurance charges of the top 10 systemic firms in 2007 by $mm (2004-2007)

Note that the insurance charges are highest in 2004, reflecting the higher volatility during this

600

Note that the insurance charges are highest in 2004, reflecting the higher volatility during this period. In this sample, volatility was highest at the beginning of the boom and lowest going into the bust, producing countercyclical insurance charges.

500MORGAN STANLEY DEAN WITTER & COCITIGROUPINC

300

400CITIGROUP INCMERRILL LYNCH & CO INCJPMORGAN CHASE & COGOLDMAN SACHS GROUP INCFEDERALHOMELOANMORTGAGECORP

100

200

FEDERAL HOME LOAN MORTGAGE CORPFEDERAL NATIONAL MORTGAGE ASSNLEHMAN BROTHERS HOLDINGS INCBEAR STEARNS COMPANIES INCMETLIFE INC

0

100

2004 2005 2006 2007

BANK OF AMERICA CORP

2004 2005 2006 2007

Rank 2004 2005 2006 2007

1FEDERAL NATIONAL MORTGAGE ASSN

FEDERAL NATIONAL MORTGAGE ASSN

MORGAN STANLEY DEAN WITTER & CO

MORGAN STANLEY DEAN WITTER & CO

2MORGAN STANLEY DEAN WITTER & CO

MORGAN STANLEY DEAN WITTER & CO

FEDERAL NATIONAL MORTGAGE ASSN CITIGROUP INC2WITTER & CO & CO ASSN CITIGROUP INC

3 JPMORGAN CHASE & COFEDERAL HOME LOAN MORTGAGE CORP GOLDMAN SACHS GROUP INC MERRILL LYNCH & CO INC

4MERRILL LYNCH & CO INC JPMORGAN CHASE & CO MERRILL LYNCH & CO INC JPMORGAN CHASE & CO

5GOLDMAN SACHS GROUP INC MERRILL LYNCH & CO INC JPMORGAN CHASE & CO GOLDMAN SACHS GROUP INCLEHMAN BROTHERS HOLDINGS LEHMAN BROTHERS HOLDINGS FEDERAL HOME LOAN

6LEHMAN BROTHERS HOLDINGS INC GOLDMAN SACHS GROUP INC

LEHMAN BROTHERS HOLDINGS INC

FEDERAL HOME LOAN MORTGAGE CORP

7PRUDENTIAL FINANCIAL INCLEHMAN BROTHERS HOLDINGS INC METLIFE INC

FEDERAL NATIONAL MORTGAGE ASSN

8CITIGROUP INC PRUDENTIAL FINANCIAL INC BEAR STEARNS COMPANIES INCLEHMAN BROTHERS HOLDINGS INC

BEAR STEARNS COMPANIES9BEAR STEARNS COMPANIES INC METLIFE INC PRUDENTIAL FINANCIAL INC BEAR STEARNS COMPANIES INC

10METLIFE INC CITIGROUP INCHARTFORD FINANCIAL SVCS GROUP I METLIFE INC

11HARTFORD FINANCIAL SVCS GROUP I BEAR STEARNS COMPANIES INC CITIGROUP INC BANK OF AMERICA CORP

12BANK OF AMERICA CORP BANK OF AMERICA CORP BANK OF AMERICA CORP PRUDENTIAL FINANCIAL INC

13WACHOVIA CORP 2ND NEWAMERICAN INTERNATIONAL GROUP IN WASHINGTON MUTUAL INC

HARTFORD FINANCIAL SVCS GROUP I

14WASHINGTON MUTUAL INCHARTFORD FINANCIAL SVCS GROUP I COUNTRYWIDE FINANCIAL CORP COUNTRYWIDE FINANCIAL CORP

15LINCOLN NATIONAL CORP IN WACHOVIA CORP 2ND NEW WACHOVIA CORP 2ND NEW WACHOVIA CORP 2ND NEW15LINCOLN NATIONAL CORP IN WACHOVIA CORP 2ND NEW WACHOVIA CORP 2ND NEW WACHOVIA CORP 2ND NEW

RANKINGS of MOST SYSTEMIC FINANCIAL INSTITUTIONS BY HYPOTHETICAL $ INSURANCEC A G S f 2004 200CHARGES from 2004-2007

Rank 2004 2005 2006 20071BEAR STEARNS COMPANIES INC BEAR STEARNS COMPANIES INC BEAR STEARNS COMPANIES INC BEAR STEARNS COMPANIES INC

2GENWORTH FINANCIAL INCFEDERAL HOME LOAN MORTGAGE CORP

FEDERAL NATIONAL MORTGAGE ASSN

FEDERAL HOME LOAN MORTGAGE CORP

LEHMAN BROTHERS HOLDINGS FEDERAL NATIONAL MORTGAGE MORGAN STANLEY DEAN WITTER LEHMAN BROTHERS HOLDINGS3LEHMAN BROTHERS HOLDINGS INC

FEDERAL NATIONAL MORTGAGE ASSN

MORGAN STANLEY DEAN WITTER & CO

LEHMAN BROTHERS HOLDINGS INC

4PRUDENTIAL FINANCIAL INCMORGAN STANLEY DEAN WITTER & CO

LEHMAN BROTHERS HOLDINGS INC MERRILL LYNCH & CO INC

5MORGAN STANLEY DEAN WITTER & CO LINCOLN NATIONAL CORP IN GOLDMAN SACHS GROUP INC

MORGAN STANLEY DEAN WITTER & CO

6LINCOLN NATIONAL CORP INLEHMAN BROTHERS HOLDINGS INC MERRILL LYNCH & CO INC

FEDERAL NATIONAL MORTGAGE ASSN

7FEDERAL NATIONAL MORTGAGE ASSN GOLDMAN SACHS GROUP INC METLIFE INC GOLDMAN SACHS GROUP INC

8HARTFORD FINANCIAL SVCS GROUP I MERRILL LYNCH & CO INC

HARTFORD FINANCIAL SVCS GROUP I COUNTRYWIDE FINANCIAL CORP8GROUP I MERRILL LYNCH & CO INC GROUP I COUNTRYWIDE FINANCIAL CORP

9METLIFE INCHARTFORD FINANCIAL SVCS GROUP I PRUDENTIAL FINANCIAL INC METLIFE INC

10MERRILL LYNCH & CO INC PRUDENTIAL FINANCIAL INC LINCOLN NATIONAL CORP INHARTFORD FINANCIAL SVCS GROUP I

11GOLDMAN SACHS GROUP INC GENWORTH FINANCIAL INC AMERIPRISE FINANCIAL INCPRINCIPAL FINANCIAL GROUP INC11GOLDMAN SACHS GROUP INC GENWORTH FINANCIAL INC AMERIPRISE FINANCIAL INC INC

12 JPMORGAN CHASE & CO METLIFE INC COUNTRYWIDE FINANCIAL CORP LINCOLN NATIONAL CORP IN

13PRINCIPAL FINANCIAL GROUP INC PRINCIPAL FINANCIAL GROUP INC JPMORGAN CHASE & CO PRUDENTIAL FINANCIAL INC

14E TRADE FINANCIAL CORP JPMORGAN CHASE & CO UNUM GROUP JPMORGAN CHASE & CO

15UNUM GROUP E TRADE FINANCIAL CORP SOVEREIGN BANCORP INC CITIGROUP INC15UNUM GROUP E TRADE FINANCIAL CORP SOVEREIGN BANCORP INC CITIGROUP INC

16TRAVELERS COMPANIES INC UNUM GROUP PRINCIPAL FINANCIAL GROUP INC AMERIPRISE FINANCIAL INC

17C I G N A CORP WASHINGTON MUTUAL INC E TRADE FINANCIAL CORP E TRADE FINANCIAL CORP

18SOVEREIGN BANCORP INC C N A FINANCIAL CORP WASHINGTON MUTUAL INC C I T GROUP INC NEW

19WASHINGTON MUTUAL INC COUNTRYWIDE FINANCIAL CORP COMMERCE BANCORP INC NJ WASHINGTON MUTUAL INC

20COMMERCE BANCORP INC NJ COMMERCE BANCORP INC NJ HUNTINGTON BANCSHARES INC COMMERCE BANCORP INC NJ

RANKINGS of MOST SYSTEMIC FINANCIAL INSTITUTIONS BY HYPOTHETICAL $ INSURANCE CHARGES from 2004-2007 as a % of EQUITY

Conclusion

Departure needed from regulation that focuses solely on institution-level risk

• Not only does it not serve its purpose, it is in fact likely to do harm!

It’s time to quantify systemic risk of financial institutions

• Marginal Expected Shortfall offers one measure

• Robustness of different systemic risk measures should be evaluated carefullyy y

• Confidence in measures would lead to eventual pricing/taxing of systemic risk contributions

S i i k CAN b d d i i k i CAN b i d d bl i Systemic risk CAN be measured and systemic risk insurance CAN be priced under reasonable assumptions

Private participation in measurement and pricing of systemic risk likely to be beneficial to regulators

• Market scrutiny of systemic risk

• Price discovery of systemic risk insurance given likely non-linearities

• Regulators can build in an automatic reinsurance through our public-cum-private scheme

51