graduate school of business global risk management: …36 •goldman sachs 2014 bs –9.67% equity...

GRADUATE SCHOOL OF BUSINESS

Global Risk Management: A Quantitative Guide

Securitization

Ren-Raw ChenFordham University

2

• What

– To make a large asset transactable

• How

– divisibility細小化

– standardization規格化

– liquidity流動性

• Why

– shift risk

– reduce risk (diversification)

Introduction

3

• Who

– Mortgage backed securities

– Credit derivatives

• When

– After WW2

– GNMA, FNMA, FHLMC

• Where

– United States

Introduction

4

• Classification

– Mortgages

– Financial loans

• Old fashion (not really satisfies 3 hows)

– leasing

– asset swaps

• Modern

– investment banks

Introduction

5

• Origin

– Passthroughs

• Ginnie Mae guaranteed the first mortgage pass-through security of an approved lender in 1968.

• In 1971, Freddie Mac issued its first mortgage pass-through, called a participation certificate, composed primarily of private mortgages.

• In 1981, Fannie Mae issued its first mortgage pass-through, called a mortgage-backed security.

Mortgage backed securities

6

• Origin

– CMOs

• first created in 1983 by the investment banks Salomon Brothers and First Boston for the U.S. mortgage liquidity provider Freddie Mac.


7

• Issuer

– agency vs. non-agency

• Property

– Residential (RMBS) vs. Commercial (CMBS)

• Credit quality

– Prime vs. Alt-A vs. Sub-prime

• Slicing (partition)

– PT vs. CMO (vs. IO/PO) vs. other types (MBB)


8

• Banks no longer suffer mis-matched risks

• Banks make servicing fees

• Banks can grow in size

• FRM (fixed rate mortgage) becomes possible

– affordable mortgage

– American dream


9

• Mortgage securitization complete capital markets

– Investors decide mortgage rates


CAPM

MBS

Treasury

stocks

RE

10

• Agencies provide credit guarantees

– charge a fee

– guaranteed by the U.S. government

• no more, FNMA and FHLMC are private

• but bailed out in 2008

• not sure what they are!

– investors worry no default risk


11

RMBS underwriting process

mortgages banks GNMA individuals

primary

market

secondary

market underwriting

dealers

12

Mortgage pool

1 - $1342.05

2 - $1342.05

$13.42 mil

$13.42 mil

$13.42 mil 360 - $1342.05

13

Partition (slicing) $13.42 mil

$13.42 mil

$13.42 mil

14

• RMBS suffers no default risk (agencies)

– non-agency RMBS do suffer default risk

– but suffer prepayment risk

• lower rates, faster prepayment -> investors lose interest income

• higher rates, slower prepayment

• Causes of prepayement

– economical: refinance

– non-economical: divorce/marriage, new child, relocation (jobs), etc.


15

• Prepayment models

– between 0~1

– tangent function (90-degree rotation)

– S curve (response function)

– logit/probit function

– economic modeling (Andrew-Davidson)


16

• To qualify for prime mortgage:

– LTV (loan-to-value ratio) < 0.8

– PI (payment-income ratio) < 1/3

– FICO > 620 (660-720-850)

• Fair, Isaac, and Company in 1989

– etc.


17

• Alt-A and subprime residential mortgages

– default risk

– non-agency

– small percentage (4% before crisis, 10~15% during crisis)

• Mortgage size $13.58 trillion

• $543 billion before crisis and $1~2 trillion after crisis

• S&L crisis $402~407 billion in 1995 (like $754 billion in 2008)


unknown:

http://www.federalreserve.gov/econr

esdata/releases/mortoutstand/current.htm

unknown:

http://www.federalreserve.gov/econr

esdata/releases/mortoutstand/current.htm

18

• CMBS is different

– non-agency

– default risk

– no prepayment risk (prepayment penalty, known as maintenance yield)

– examples: casinos, hotels, rentals, shopping centers, parking lots, auto floorspace, etc.


19

• Finally there is ABS

– credit card loans,

– home equity (HE) loans,

– auto loans,

– student loans (Sallie Mae - Student Loan Marketing Association),

– agriculture loans (Farmer Mac - Federal Agricultural Mortgage Corporation)


20

• An important type of MBS

• Waterfall

• Give birth to CDO

Collateral mortgage obligation

21

Collateral Debt Obligation (CDO)

22

• Types

– cash CDO (real bonds)

– synthetic CDO (CDS)

• by action

– cash-flow CDO (boxed)

– market-value CDO (non-boxed)

• by sponsor

– arbitrage CDO (active)

– balance-sheet CDO (passive)


23

– http://thismatter.com/money/bonds/types/cdo.htm


24

• CDX

– a CDX CDO is a CDO with 125 credit default swaps (8% each) with US$10 million notional

– 0-3%, 3-7%, 7-10%, 10-15%, and 15-30%.

– very liquid (more liquid than single name CDS)


25

• Waterfall


Tranche loss

Equity tranche

Mezzanine tranche

Senior tranche

Total loss

1K

2K

0K

3K

26

• Risky bond + CDS = risk-free bond

– hence, risky bond = risk-free bond - CDS

– i.e. risky bond = long risk-free bond and short CDS (provide protection)

– e.g. $100 mil risky bonds = $100 mil Treasury and $100 mil CDS (which has no value)

– Treasury is collateral

• if no collateral, then no treasury

Synthetic CDO

27

Synthetic CDO

POOLPOOL

AA

BB

ZZ

CDSCDS

CDSCDS

CDSCDS

$1,250

million

$1,250

million

default

28

$1,250 million

$6 million

$4 million

LOSS

WDFA

29

• Sizing

– key to securitization

– size of tranches = size of pool

– cash flows from tranches = cash flows to CDS pool

– match market spreads, for example

• AAA 20bps, AA 50 bps, A 80 bps, BBB 120 bps, etc.

– each tranche is given a size, a rating, and a spread

Synthetic CDO

30

• Copula (how to correlate defaults)

– Gaussian copula (solve the dependency problem)

– Key equations


ˆ ˆ1i M ix W Wρ ρ= + −

� ( ) � ( )

� ( )

( )

( )1

|

1

1

ˆ( )

1

ˆ Pr | Pr 1

Pr i

i

i

i f i i M i i

f K

i

K f

N p f

p x K W f f W K

W

N

N

ρ

ρ

ρ

ρ

ρ

ρ

ρ ρ

−

−

−

−

−

−

−

= < = = + − <

<=

=

=

31

– Loss distribution

• Fourier inversion

• Recursive algorithm


prob(rho=0)

-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 60

Loss

prob(rho=0.5)

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 60

Loss

prob(rho=0.9)

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 60

Loss

32

• Problems with such a loss distribution

– thin tranches (100 tranche CDO)

– CDO^2, CDO^3, ...

– mezzanine tranches difficult to price


33

• Tranche loss/spread plots here


34

• A Cat analogy

– Cats have nine lives (JPM)


35

• Securitization caused crisis

– Off-balancesheet transactions: swaps (IRS/CDS)

– Over-leverage

• Securitization is a solution to relieve capital

Crisis and securitization

36

• Goldman Sachs 2014 BS

– 9.67% equity

– To reach 15% ROE

Crisis and securitization

Cash And Cash Equivalents 237,254,000 0%

Net Receivables 123,417,000 0.25%

Other Assets 22,599,000 4%

Long Term Investments 472,970,000 4%

Total Assets 856,240,000 2.35%

Total Liabilities 773,443,000 1.50%

Total Stockholder Equity 82,797,000 10.30%

Total Assets 856,240,000 2.35%

Total Liabilities 802,256,782 1.50% 93.70%

Total Stockholder Equity 53,983,218 15.00% 7.30%

Cash And Cash Equivalents 237,254,000 0%

Net Receivables 123,417,000 0.25%

Other Assets 22,599,000 4%

Long Term Investments 472,970,000 4%

Total Assets 856,240,000 2.35%

Total Liabilities 773,443,000 2.00%

Total Stockholder Equity 82,797,000 5.63%

Total Assets 856,240,000 2.35%

Total Liabilities 883,112,981 2.00% 97.30%

Total Stockholder Equity 53,983,218 15.00% 2.70%

37

• China’s bond market - world’s largest

– Yet not liquid

• U.S. fixed income markets

– Treasuries ($16 trillion), residential mortgages ($15 trillion), corporate debts (?? $40 trillion), bank loans (??)

– IRS $500 trillion

– CDS $30+ trillion

– Other swaps (asset swaps, index swaps, TRS, etc.)

Securitization in China

38

• Cash flow projection

• Cash flow matching

• Market valuation of assets

– If not then model valuation

• Optimization

– Hopefully 1-1>0

Securitization How

39

• Lehman

– $4 on Friday, bankruptcy on Sunday

– Summer trouble started

– Prior to Labor day talked to KDB

– Tuesday (9/9) stock fell 45%

– Thursday (9/11) JPM demanded $3 bn

• Starr-Merrill Deal (7¢ out of $1)

• Buffett buying GS $115

Extreme Liquidity Cases

40

• Bear

– March 14, 2008: Bailout talk began, stock at $30

– March 16: $2

– March 17: $10

• AIG

• Wachovia

• Morgan Stanley

Extreme Liquidity Cases

41

• If you have to provide liquidity, how much do you charge?

– Value of liquidity – liquidity quantification

– Liquidity gap management (A vs. A*)

Value of liquidity

42

• Gamma analogy

– Perfect liquidity = delta hedging

– Example: ATM option near maturity

– Lose money

• Always sells when price is low (b/c delta is low)

• Always buys when price is high (b/c delta is high)

– Money lost = liquidity premium

– Higher gamma securities ~ higher liquidity risk

Basic Ideas

43

• Demand/Supply squeeze

– Liquidity is option

– Theoretical framework

• Illiquid price is liquid price +/- an option

– An example

• Equilibrium pricing

– An example

– CAPM for the utility

Basic Ideas

44

• Italy

Liquidity and Term Structure

45

• Spain


46

• Greece


47

• Liquidity Default (Going Concern)

– Enough liquid assets to pay for imminent cash obligation, K1

– A* > K1

• Economic Default (Geske/Leland)

– Negative equity

– I.e., Any time a firm can issue equity

– A > Sum of all debts

– E > K1 (Geske)

Application 2: Pricing Illiquid Assets

48

• Under illiquidity

– Assets are LVA, 0 or partial liquidity (m)

– LVA = cash + a*MVFA

• as MVE drops, m drops, a drops (MVFA fixed)

– Q1 represents liquidity-tampered economic PD


49

• Chen model for liquidity discount

– risk aversion

– market information (vols, prices, etc.)

• Geske model for corporate finance

– based upon Black-Scholes-Merton

– multiple debts


50


51


52

• In March,

– Market Cap = $20.75 Billion

– Market Value of Assets = $115 Billion

– Default probability high

– Assume an equity infusion that increases cash by a like amount - assets increases assets but no increase in debt

– Lehman needs to raise equity nearly equal to 30% of its assets to reduce default probability below 10%


53

• Lehman actions

– Lehman raised $4 billion in April and $6 billion in June

– Volatility fell from over 150% to 90% to under 55% during this period of time

– Default probabilities fell as well during this period, however, clearly it was not enough

– Both volatility and default probabilities spiked again in July and August just prior to Lehman’s bankruptcy filing


54

• In order to decrease the risk of default much larger amounts of capital would have been needed

• Raising capital is only one tool [albeit an important one]


55

• Data

– Comprehensive corporate debt dataset from FactSet

• Once a month from 12/07 to 08/08

• Filter the data by issue date and redemption date to get all outstanding debt as of specified day

• At any given time Lehman had several thousand different bond issues outstanding

– Stock information

• price and volatility


56

• Lehman asset values (liq vs. ill.)


0

50000

100000

150000

200000

250000

300000

350000

400000

1/2

/2004

3/2

/2004

5/2

/2004

7/2

/2004

9/2

/2004

11/2

/2004

1/2

/2005

3/2

/2005

5/2

/2005

7/2

/2005

9/2

/2005

11/2

/2005

1/2

/2006

3/2

/2006

5/2

/2006

7/2

/2006

9/2

/2006

11/2

/2006

1/2

/2007

3/2

/2007

5/2

/2007

7/2

/2007

9/2

/2007

11/2

/2007

1/2

/2008

3/2

/2008

5/2

/2008

7/2

/2008

A0

A0*

57

• The failure of Lehman Brothers represents a major inflection point in the financial crisis and an instance where our model would have been particularly useful

– Excessive leverage

– Uncertainty about asset values

– Passive in recapitalizing

– Lax regulatory requirements


58

• Regulator

– Reduces default probability

– If insufficient capital is raised, firm has an incentive to increase the volatility of assets to increase value of equity

– Increase volatility increases default risk

– BOTTOM LINE: Regulators have to target both volatility and capital ratio in order to reduce default risk


59

• Conclusion

– A fully-endogenous structural credit risk model can be used for determining the capital adequacy of financial institutions

– Can accommodate complex capital structures

– Especially useful during rapidly changing market conditions, i.e. in a financial crisis


60

• Conclusion

– Debt is serviced by issuing new equity

– For a maximum acceptable default probability we can solve for the minimum amount of equity that the financial institution would have to raise


61

• Lehman unaffected


62

• Lehman like


62

63

• Lehman spillover


64

• FHLMC and FNMA


65

• BanksLiquidity Discount Ratio Index (All Banks)

monthly data from Jan 1997

0

0.2

0.4

0.6

0.8

1

1.2

9701

9705

9709

9801

9805

9809

9901

9905

9909

0001

0005

0009

0101

0105

0109

0201

0205

0209

0301

0305

0309

0401

0405

0409

0501

0505

0509

0601

0605

0609

0701

0705

0709

0801

0805

0809

0901

0905

0909

1001

1005

1009

1101

1105

dis

cou

nt

rati

o

mkt_weight

eql_weight

median

Liquidity Discount Ratio Index (US Commercial Banks)monthly data from Jan 1997

0

0.2

0.4

0.6

0.8

1

1.2

9701

9705

9709

9801

9805

9809

9901

9905

9909

0001

0005

0009

0101

0105

0109

0201

0205

0209

0301

0305

0309

0401

0405

0409

0501

0505

0509

0601

0605

0609

0701

0705

0709

0801

0805

0809

0901

0905

0909

1001

1005

1009

1101

1105

dis

cou

nt

rati

o

mkt_weight

eql_weight

median

Liquidity Discount Ratio Index (Non-US Commercial Banks)monthly data from Jan 1997

0

0.2

0.4

0.6

0.8

1

1.2

9701

9705

9709

9801

9805

9809

9901

9905

9909

0001

0005

0009

0101

0105

0109

0201

0205

0209

0301

0305

0309

0401

0405

0409

0501

0505

0509

0601

0605

0609

0701

0705

0709

0801

0805

0809

0901

0905

0909

1001

1005

1009

1101

1105

dis

cou

nt

rati

o

mkt_weight

eql_weight

median

Application 3: Liquidity Index

66

• BanksLiquidity Discount Ratio Index (Eastern-US Commercial Banks)


0

0.2

0.4

0.6

0.8

1

1.2

9701

9705

9709

9801

9805

9809

9901

9905

9909

0001

0005

0009

0101

0105

0109

0201

0205

0209

0301

0305

0309

0401

0405

0409

0501

0505

0509

0601

0605

0609

0701

0705

0709

0801

0805

0809

0901

0905

0909

1001

1005

1009

1101

1105

dis

cou

nt

rati

o

mkt_weight

eql_weight

median

Liquidity Discount Ratio Index (Southern-US Commercial Banks)monthly data from Jan 1997

0

0.2

0.4

0.6

0.8

1

1.2

9701

9705

9709

9801

9805

9809

9901

9905

9909

0001

0005

0009

0101

0105

0109

0201

0205

0209

0301

0305

0309

0401

0405

0409

0501

0505

0509

0601

0605

0609

0701

0705

0709

0801

0805

0809

0901

0905

0909

1001

1005

1009

1101

1105

dis

cou

nt

rati

o

mkt_weight

eql_weight

median

Liquidity Discount Ratio Index (Western-US Commercial Banks)monthly data from Jan 1997

0

0.2

0.4

0.6

0.8

1

1.2

9701

9705

9709

9801

9805

9809

9901

9905

9909

0001

0005

0009

0101

0105

0109

0201

0205

0209

0301

0305

0309

0401

0405

0409

0501

0505

0509

0601

0605

0609

0701

0705

0709

0801

0805

0809

0901

0905

0909

1001

1005

1009

1101

1105

dis

cou

nt

rati

o

mkt_weight

eql_weight

median


67

• BanksLiquidity Discount Ratio Index (Commercial Banks Central US)


0

0.2

0.4

0.6

0.8

1

1.2

9701

9705

9709

9801

9805

9809

9901

9905

9909

0001

0005

0009

0101

0105

0109

0201

0205

0209

0301

0305

0309

0401

0405

0409

0501

0505

0509

0601

0605

0609

0701

0705

0709

0801

0805

0809

0901

0905

0909

1001

1005

1009

1101

1105

dis

cou

nt

rati

o

mkt_weight

eql_weight

median

Liquidity Discount Ratio Index (Regional Non-US Banks)monthly data from Jan 1997

0

0.2

0.4

0.6

0.8

1

1.2

9701

9705

9709

9801

9805

9809

9901

9905

9909

0001

0005

0009

0101

0105

0109

0201

0205

0209

0301

0305

0309

0401

0405

0409

0501

0505

0509

0601

0605

0609

0701

0705

0709

0801

0805

0809

0901

0905

0909

1001

1005

1009

1101

1105

dis

cou

nt

rati

o

mkt_weight

eql_weight

median

Liquidity Discount Ratio Index (Super Regional Banks US)monthly data from Jan 1997

0

0.2

0.4

0.6

0.8

1

1.2

9701

9705

9709

9801

9805

9809

9901

9905

9909

0001

0005

0009

0101

0105

0109

0201

0205

0209

0301

0305

0309

0401

0405

0409

0501

0505

0509

0601

0605

0609

0701

0705

0709

0801

0805

0809

0901

0905

0909

1001

1005

1009

1101

1105

dis

cou

nt

rati

o

mkt_weight

eql_weight

median


68

• BanksLiquidity Discount Ratio Index (Mortgage Banks)


0

0.2

0.4

0.6

0.8

1

1.2

9701

9705

9709

9801

9805

9809

9901

9905

9909

0001

0005

0009

0101

0105

0109

0201

0205

0209

0301

0305

0309

0401

0405

0409

0501

0505

0509

0601

0605

0609

0701

0705

0709

0801

0805

0809

0901

0905

0909

1001

1005

1009

1101

1105

dis

cou

nt

rati

o

mkt_weight

eql_weight

median

Liquidity Discount Ratio Index (Fiduciary Banks)monthly data from Jan 1997

0

0.2

0.4

0.6

0.8

1

1.2

9701

9705

9709

9801

9805

9809

9901

9905

9909

0001

0005

0009

0101

0105

0109

0201

0205

0209

0301

0305

0309

0401

0405

0409

0501

0505

0509

0601

0605

0609

0701

0705

0709

0801

0805

0809

0901

0905

0909

1001

1005

1009

1101

1105

dis

cou

nt

rati

o

mkt_weight

eql_weight

median

Liquidity Discount Ratio Index (Diversified Banks)monthly data from Jan 1997

0

0.2

0.4

0.6

0.8

1

1.2

9701

9705

9709

9801

9805

9809

9901

9905

9909

0001

0005

0009

0101

0105

0109

0201

0205

0209

0301

0305

0309

0401

0405

0409

0501

0505

0509

0601

0605

0609

0701

0705

0709

0801

0805

0809

0901

0905

0909

1001

1005

1009

1101

1105

dis

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rati

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mkt_weight

eql_weight

median


69

• Use the models to build a risk metric for liquidity risk

– Help decide if a portfolio is liquid (trading book) or not (banking book)

• Existing Indices

– Chordia, Roll and Subrahmanyam (2001), Hasbrouck and Seppi (2001), Amihud (2002), Jones (2002), Huberman and Halka (2001)


70

• Use leverage

• Use volume

• Use credit spreads

• Use bid-offer

• A ratio between 0 and 1


71

• Many ways to generate high correlation

– Jumps

– common factor

– etc.

• Liquidity-driven correlation

– good measure for systemic risk

– good driver for stress tests

– good for a liquidity barometer

Application 4: Systemic risk

72

• Liquidity like a common factor but

– non-linear

• better capture large swings

– endogenous (less ad-hoc)

• better capture feedback effects

– stable (part of structure)

• better than regressions

– nice link to the theory – put option premium

• easier to model


73

• A simple model for liquidity squeeze

– K is related to convexity (liquidity risk). When K is high, risk is high.

– x = X – P; y = Y – Q;

X Y K

Sigma 0.25 0.40 0.25

P_X, P_K 0.45 0.45

Q_Y, Q_K 0.08 0.84

Price 50 80 50


74

• Liquidity-constrained correlation vs. unconstrained correlation

2 2

2 2 2 2 2 2

2 2 2 2 2 2

cov[ , ] (1 )(1 )

var[ ] (1 )

var[ ] (1 )

cov[ , ]corr[ , ]

var[ ]var[ ]

X Y X Y K K K

X X K K

Y Y K K

dx dy XY P Q P Q K

dx X P P K

dy Y Q Q K

dx dydx dy a b

dx dy

σ σ ρ σ

σ σ

σ σ

ρ

= − − +

= − +

= − +

= = +


75

• K from 0 ~ 200 (small to large)

-0.4

-0.2

-0.1

0.0

8

0.2

4

0.4

1

50

120

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1


76

• Vol from 0.1 ~ 4 (small to large)

-0.4-0

.2-0.10.0

8

0.2

40.4

0.10.3

0.71.2

2

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1


77

• K-sigma from 0.1 ~ 4 (small to large)

-0.4

-0.2

-0.1

0.0

8

0.2

4

0.4

0.1

0.7

2

-0.5

0

0.5

1


78

• Define scenarios

– historical

• historically worst in a defined period

– hypothetical

• stress parameters

– ideal

• stress economic variables, such as liquidity

Application 5: Stress Testing

79

• Price-volume

– K is related to quantity. When K is high, quantity is low.

– ∆P/∆Q is more severe under liquidity squeeze.

0

10

20

30

40

50

60

70

80

0 10 20 30 40 50 60 70

Liquid PQ

Illiquid PQ

Quantity

Price


80

• Example: Morgan Stanley


0

10000000

20000000

30000000

40000000

50000000

60000000

5/4/

1999

3/1/

2000

1/2/

2001

11/1

/2001

9/3/

2002

7/1/

2003

5/3/

2004

3/1/

2005

1/3/

2006

11/1

/2006

9/4/

2007

7/1/

2008

5/1/

2009

3/1/

2010

1/3/

2011

11/1

/2011

9/4/

2012

7/1/

2013

0

10

20

30

40

50

60

70

80

Volume

Price

81

Name

Drop in

Model-

implied

Asset

Value

Drop in

Equity

Value

Model-

implied

Spread

Quantity

discount

AIG 1.02% 21.66% 1.50% 48.04%

ALL 1.02% 17.52% 1.50% 48.04%

AXP 1.02% 22.99% 1.50% 48.04%

BAC 3.62% 65.01% 6.04% 64.81%

BBT 1.02% 27.87% 1.50% 52.64%

BK 1.02% 26.03% 1.50% 52.64%

BRK.A 1.02% 16.71% 1.50% 48.04%

C 3.22% 59.25% 4.86% 63.53%

COF 1.02% 25.93% 1.50% 52.64%

FITB 1.02% 23.77% 1.50% 48.04%

GNW 3.88% 38.59% 3.81% 56.59%

GS 1.03% 26.08% 1.51% 52.64%

PFG 1.02% 18.40% 1.50% 48.04%

PNC 1.02% 28.32% 1.50% 52.64%

PRU 1.08% 28.95% 1.58% 52.64%

SLM 21.82% 97.46% 12.86% 65.35%

STI 1.04% 28.48% 1.53% 52.64%

STT 1.03% 29.68% 1.51% 52.64%

TRV 1.02% 16.56% 1.50% 48.04%

USB 1.02% 22.78% 1.50% 48.04%


82

• Decrease in volume when market liquidity dries up

– estimate the loss of value to reach $820 billion

– part of this loss is due to price drop and the remaining part of it (quantity drop) is the amount of wealth gets transferred out of the equity market and flown to the Treasuries market, known as fly to quality. This value is estimated to be $650 billion, which is half of the pre-crashed market value.


83

• CDX vs. SPY

– Duration neutral and play on convexity

Application 6: Cap.Str.Arb.

84

• http://www.ey.com/GL/en/Industries/Financial-Services/Banking---Capital-Markets/Basel-III-liquidity-requirements-and-implications---Regulatory-rules-operational-and-strategic-implications

• http://www.bis.org/publ/bcbs144.htm

• http://www.federalreserve.gov/boarddocs/srletters/2010/sr1006a1.pdf

• http://www.bis.org/publ/bcbs219.pdf

• www.afme.eu/WorkArea//DownloadAsset.aspx?id=10332

Appendix

graduate school of business global risk management: …36 •goldman sachs 2014 bs –9.67% equity...

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