Some Comments Lewis 2008 Some Comments Lewis 2008

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Some Comments Lewis (2008)

Some Comments Lewis (2008)

Some Comments Lewis (2008) u Interesting narrative authored by (quasi) insider u Timely publication

Some Comments Lewis (2008) u Interesting narrative authored by (quasi) insider u Timely publication u Fun to read (relative to academic pieces) u But, focus “great man” (Eisman) versus “boogie man” (Gutfreund) – Only tidbits of NIE questios posed…

NIE Perspectives On Credit Crisis u u Contract externalities? – Structured investment vehicles may

NIE Perspectives On Credit Crisis u u Contract externalities? – Structured investment vehicles may create a form of inter se simplicity (more later), but macro-complexity (Gorton, ‘ 08) Agency Costs: Structuring => cash flow rights ≠ control rights? – But why wasn’t this priced out by purchasers? » Agency cost turtles all the way down? u u u Dearth of third party arbitrageurs (i. e. , Steve Eismans)? – Expense, Rational Bubbles, and limits to arbitrage? Ownership structure (another type of agency cost)? – Did de-partnerizing skew their incentives? (But see above) Political Economy, Oversight, and Tipping Points? – Cheap access to capital by constituents – Scale/Contingent limited liability; ultimate risk bearers?

The Origins of the Financial Crisis Eric Talley UC Berkeley Law School etalley@law. berkeley.

The Origins of the Financial Crisis Eric Talley UC Berkeley Law School [email protected] berkeley. edu

Overview of the Credit and Credit Derivative Markets (Focus – Housing Debt; Also Auto

Overview of the Credit and Credit Derivative Markets (Focus – Housing Debt; Also Auto / CC debt) u Before 1990 s: Comparatively Simple Market – Borrowers – Original Lenders / Originators – Secondary Market Players (Inc. Fannie; Freddie) » Had significant constraints on what they could purchase => significant constraints on borrowers u Beginning late 1980 s-2000 s: – Structured Finance Industry: Create value by pooling and rearranging cash flow rights to make risky assets less risky. RMBSs; CMBSs;

The value proposition? Simple* Example of an MBS A and B each owe on

The value proposition? Simple* Example of an MBS A and B each owe on $1000 face value obligations u u Prob{default} for each = 10%; complete default (i. e. , recovery rate=0) – Thus, cash flow rights are uncertain; risk averse buyers would pay at most $900 to buy mortgage (often substantially less) Idea (the “A-B MBS”): Combine A’s and B’s payments into single (BR remote) “pool”; sell off 2 cash flow claims from the combined pool: – (a) Right to collect the first $1000 to come in (senior tranche); – (b) Right to collect residual (equity tranche) If A’s default risk is independent from B’s (will come back to this) then – Senior tranche holder collects $1000 unless both A and B default, which happens only 1% of the time (10% x 10%). – Equity tranche holder collects $1000 only if neither A and B default (81% of time), and thus default happens 19% of the time. Result – Financial Alchemy: – Securitizing turns two “pretty risky” securities into one “safe” asset and another “riskier” asset. – Market premium for low-risk assets was high enough to offset discount on equity tranche. *Time permitting (it won’t), I’ll discuss a more technical description…

MBS Canonical Structure

MBS Canonical Structure

MBS Canonical Structure (II)

MBS Canonical Structure (II)

Growth of Structured Finance… u u Downside of MBS – it introduced wedge between

Growth of Structured Finance… u u Downside of MBS – it introduced wedge between owners of the cash flow rights and control rights; less transparency; added oversight “goo”… – Yet, the added goo did not get reflected in the credit ratings that the senior tranches got from ratings agencies, so people made (lots of) money… Logical Next Question – Turn the crank again? – I. e. , could one take the equity tranche of an MBS, pool it with other equity tranches of other MBSs, and sell off structured tranches? – The CDO – a credit derivative security not backed by mortgages themselves, but instead backed by securities that were themselves backed by mortgages.

Continue Example A-B MBS, and suppose that there’s an analogous MBS pooling the mortgage

Continue Example A-B MBS, and suppose that there’s an analogous MBS pooling the mortgage obligations of C & D u u For each MBS equity tranche, recall chance of default for each is 19%. – Owning rights to payment is uncertain; risk averse buyers would pay far less than $810. Combine A-B and C-D equity tranches into a single “pool”; and once again sell off two tranches: – (a) Right to collect the first $1000 to come in (senior tranche); – (b) Right to collect whatever else comes in (equity tranche) Again if default by the A-B asset is independent from the C-D asset then – Senior tranche holder collects $1000 unless both A-B and C-D default, which happens only 3. 6% of the time (19% x 19%) – Equity tranche holder collects $1000 only if neither A and B default, and thus default happens 34. 4% of the time. Result – Financial Alchemy, Squared: – We’ve turned two “pretty risky” equity tranche MBSs into one “pretty safe” asset and another “extremely risky” asset. – But with enough credit enhancements and the right structure we could make the pretty safe asset even safer.

Canonical CDO Structure

Canonical CDO Structure

Global CDO Issuance $Billion Source: Securities Industry and Financial Markets Association Source: SIFMA, UBS

Global CDO Issuance $Billion Source: Securities Industry and Financial Markets Association Source: SIFMA, UBS

“Synthetic” CDOs and Credit Default Swaps Source: wsj. com

“Synthetic” CDOs and Credit Default Swaps Source: wsj. com

Credit Default Swaps: Significant Notional Counterparty Risk 70 $Trillions Treasuries GSE MBS Corporate Equities

Credit Default Swaps: Significant Notional Counterparty Risk 70 $Trillions Treasuries GSE MBS Corporate Equities 60 CDS $62. 2 50 40 30 20 10 0 2001 2002 2003 2004 2005 2006 2007 2008 Q 2

CDS Counterparty Risk u Unregulated, privately negotiated bilateral trading structure. » No standardized capital

CDS Counterparty Risk u Unregulated, privately negotiated bilateral trading structure. » No standardized capital requirements, no standardized valuation methods, no standardized contract structure. u u u No central clearinghouse or system for recording trades. CDS positions are long and can only be “unwound” with countervailing positions. Bears Stearns, AIG, Lehman were all important “sellers” of CDS – fee businesses with significant liquidity risk – Sellers needed reliable access to short term debt markets (repo; corporate paper) to fund their CDS activities (fees; collateral calls)

Key Events Precipitating Crisis (I) 2001 - early 2007 u Profitability of Structured Finance

Key Events Precipitating Crisis (I) 2001 - early 2007 u Profitability of Structured Finance worked its way back down the supply chain – Pressure for more low risk MBSs => Pressure for more mortgages => incentives for brokers => incentives for borrowers to refinance – Reduced documentation, income, LTV requirements » Some thought this made sense, if the structured finance market was indeed engineer unsystematic risk out of the underlying assets. » But securitized loans appear to have had very distinct risk characteristics than their non-securitized counterparts…

Did Securitization Invite Reckless Lending? Default rates for subprime loans just above / below

Did Securitization Invite Reckless Lending? Default rates for subprime loans just above / below standard securitization threshold (origination b/t 2001 -06) Source: Keys et al (2008)

Key Events Precipitating Crisis (II) Late 2007 - 2008 u u u Housing Bubble

Key Events Precipitating Crisis (II) Late 2007 - 2008 u u u Housing Bubble began to deflate nationally, with three big effects: – Usual exit option (refinancing prepayments) no longer viable – Balloon payments (designed to encourage refinancing) now put strain on borrowers who couldn’t refinance out. Defaults increase – Defaults more highly correlated nationally than theretofore presumed. Exacerbating Effects – Recovery rates / Loss given default begin to rise… – Significantly: It wasn’t known (and still isn’t) how much higher both defaults and recovery rates will go…. Great uncertainty about how to price risk because of unknown correlation. Liquidity Chickens & Roosting / Credit Rationing – CDS/CDO indicatives plummet => Collateral calls increase – Protection sellers must sell mortgage backed derivative assets to raise cash and avoid default. – Short term debt market (commercial paper / repo) dries up for CDS sellers. – Required to update accounting statements to “mark to market”, inducing bankruptcy

Litigation Risks: How much litigation has the credit / subprime credit crisis spawned? u

Litigation Risks: How much litigation has the credit / subprime credit crisis spawned? u u u Individual Litigation – Breach of contract, common law fraud, unfair trade practices – Numerous state actions under state securities laws Corporate / Securities Litigation – Depending on how one counts, between federal 140 -170 federal securities class actions filings in the past 15 months. – Breach of fiduciary duty of care/loyalty – SEC enforcement actions (e. g. , Aiding and Abetting) ERISA Litigation related to mgmt of pension / 401 k assets. “Gatekeeper” (e. g. , auditor) liability claims – Negligent audit; A&A in breach of F. D. Litigation Pertaining to TARP/Bailout Itself

Example: HSH Nordbank v. UBS Synthetic CDO Structure (Note: German Bank suing Swiss Bank

Example: HSH Nordbank v. UBS Synthetic CDO Structure (Note: German Bank suing Swiss Bank in Manhattan; CDO Created through a CDS contract tied to an underlying reference pool of CDO assets)

Sub-prime / credit crisis related class action lawsuits Market Share 22. 7% 44. 9%

Sub-prime / credit crisis related class action lawsuits Market Share 22. 7% 44. 9% 61. 4%

Extending Basic Model u u Debt obligations are not “one shot” deals cash flows

Extending Basic Model u u Debt obligations are not “one shot” deals cash flows – More generally, cash flow rights from each obligation are a stream of cash flow rights. . . Loss Given Default / Recovery Rate – Conditional on default, creditor may not be washed out, but instead may recover some “residual” value. E. g. , u Default $1000 Default $1000 $500 $500 Maturity If periodic payments occur in close enough proximity to one another, then one can approximate each cash flow stream as a “continuous” flow of rights…

Technical Detail -- Continuous “Survivorship” Models (Origins: Biology; Operations Research) u Basic Idea: –

Technical Detail -- Continuous “Survivorship” Models (Origins: Biology; Operations Research) u Basic Idea: – Loan obligation can be thought of as a continuous flow of income, which lasts up to the point that the obligor defaults or retires debt – Thus, if X denotes the (random) time of default, then the length of the cash flow stream is also X, with a cumulative distribution F(X) » E. g. , Exponential Distribution… u A 2 nd loan obligation can also be conceived as a continuous cash flow whose time of default Y has a cumulative distribution G(Y) – If we pool these two obligations together, then to value the pool (or its parts) we need to understand how / whether the component parts are related to one another » I. e. , the joint distribution H(X, Y) – Problem: we often have reliable information on only “marginal” distributions F(X); G(Y)

Key Concept: “Copula” Functions u A function that combines two (or more) marginal distributions

Key Concept: “Copula” Functions u A function that combines two (or more) marginal distributions into a the “true” joint distribution (implied by Sklar’s Theorem) – H(X, Y) = C(F(X), G(Y)) – Example: “FGM” copula with Exponential Distributions: » C = F(X)×G(Y)×(1+d×(1 -F(X))(1 -G(Y)) d=0 d>0

u u u FGM Copula – C = F(X)×G(Y)×(1+d×(1 -F(X))(1 -G(Y)) Frank Copula –

u u u FGM Copula – C = F(X)×G(Y)×(1+d×(1 -F(X))(1 -G(Y)) Frank Copula – C = (1/a) × ln [ 1 + (ea. F(X)-1) (ea. G(Y) -1) / (ea -1) ] Gaussian (Normal Distribution) Copula – C = F (F-1 (F(X)), F-1(G(Y)), r) Many others…see Wikipedia (seriously) – While there are infinitely many copulas out there, most finance quants tended to use the Gaussian copula, as it has reasonably good mathematical (as opposed to empirical) properties – With a good computer, one can also easily implement it for 3, 4, 5, …. 1000 different cash flow streams E. g. , David Li (infamous working paper, 2001)

Do copula approaches work for valuing credit derivatives? u u u Yes, they are

Do copula approaches work for valuing credit derivatives? u u u Yes, they are good approximations… …but using them presupposes that one knows: 1. Correct representation of cash flow rights/obligations 2. Correct marginal distributions to use; 3. Correct copula function to use for combining marginals; 4. Correct parameters to feed into the copula (e. g. , value of d) Lots of model uncertainty here. Perilous simplifications – No explicit modeling of liquidity risks (only default risks) – Presumption of constant recovery rates – – Estimating correlation parameters using only “normal” (possible bubble) years No attempt to