© 2012 IBM Corporation Black Swans – Systemic Risk in Finance Alan King IBM Thomas J. Watson...

© 2012 IBM Corporation© 2012 IBM Corporation

Black Swans – Systemic Risk in Finance

Alan King

IBM Thomas J. Watson Research Center

joint with Francis Parr, IBM Research


Financial Crisis – a politician's view: who said this ?

The national budget must be balanced.

The public debt must be reduced; the arrogance of the authorities must be moderated and controlled.

Debts to foreign governments must be reduced, if the nation doesn't want to go bankrupt.

People must again learn to work, instead of living on entitlements

Cicero, 55 BCRoman author, orator,and politician (106 BC - 43 BC)


Trends underlying Global Financial Crises 2007-2012

1. World Population is 6B (to 9B in 2050). Only 15% reside in Developed Countries.

2. Relative decline in ROI in Developed Economies.Approaching carrying capacity (for current technology base)

• Spikes in commodity costs whenever growth increases to past trendlines• Declining capacity to absorb externalities• Replacing infrastructure rather than building anew.

Outsourcing to Developing Economies• Relocation of “investment potential” to Dev’d Economies• Demand decline in Dev’d not replaced one-to-one by Dev’g• Increased protection costs for extended supply chains

Financial imbalances • Savings gluts in Dev’g economies need somewhere to invest. • Shortage of high-quality investments in Dev’d economies. • Asset bubbles in Dev’d countries.

3. Ongoing Financial Crises are symptoms of this transition.2007 – 2012: US mortgage securities, European sovereign debt, Chinese bonds next?


Outline

A basic model of bank intermediation

The Black Swan of 2007-8 in terms of the 4 L’s (apologies to Andy Lo)– Leverage– Losses– Linkages– Liquidity

Systemic risk infrastructure


Model of Financial Intermediation

Banks are intermediaries– Savings custodian– Loans for investment (long-term) and liquidity (short-term)– Transactions in foreign exchange, high-value payments, etc

Expected NPV model

– future outflows:

– future inflows:

– net flows:

tL

tK

ttt LKM } MQME

tt

Q ,:


Generic Valuation Operators

Default

paid

Default

P= 0.001

P= 0.05

P= 0.949

:,MP

Fair Value

:,MQ

Mark-to-Market Calibrates to swap market: interest rate normally distributed.

Spreads correspond to default risk; mark-to-market of spreads is valued inCredit Default Swap (CDS) market.

Time

Value

Q

Fair value calibrates to historical performance of similar flows.


Stochastic Programming Valuation*

1. Combines fair value and mark-to-market

2. Consistent with options pricing – risk-neutral valuation of Cox & Ross

Data: prices of n securitiesState: positions in n securitiesAction: trades

3. Dual stochastic linear programs

(P) min subject to:

(D) max subject to:

nttt ZZZ 1:

1: ttt

00 Z 0 with TTttt ZMZ

MV , 0 with 1 VZZEZ ttV

t

Primal replicates flows through trading (including options).Dual constraints permit calibration to market value of forward-looking securities (like options).

*King 2002, King-Koivu-Pennanen 2004, King-Streltchenko-Yesha 2010

nttt 1:


SP Valuation – interpretation of components

00 minimize Z

ttt MZ

0 TTZ

Minimize price – funds the “worst case” state of world

Self-financing trades replicate flows over all “states of world”

Risk of loss is modeled as “hard constraint”

MV , VRisk-neutral distribution performs “stochastic discounting” to present

“Risk neutral” valuation – “real probabilities” not required. Other risk models possible … dual objective is “distance to real distribution”

Calibration to market by incorporating “constraints”


Bank

1950’s – Good Bank “Hold to Maturity”

households

households

SaversInvestors

LiabilitiesAssets

loans

RA RL

Capital(A – L) =

Earnings per share:

(A*RA – L*RL)/C

EP

S

Asset Insurance

FNMA, FMAC, GNMA

Deposit Insurance

FDIC

Federal Reserve Bank

Money

Sup

ply

Reserves

deposits


Bank

1950’s – BAD Bank “on life support”

households

households

SaversInvestors

LiabilitiesAssets

loans

RA RL

BUST(A – L) =

Earnings per share:

(A*RA – L*RL)/C

EP

S

Asset Insurance

FNMA, FMAC, GNMA

Deposit Insurance

FDIC

Federal Reserve Bank

Reserves

deposits

$$$

BAD

$$$

$$$


Bank

2000’s – New Banking: “Originate to Distribute”

households

households

SaversInvestors

A-L =

Bundled

Loans

Asset B

acked S

ecurities

Collateralize

d Debt

Obligations

Rated B

onds and N

otes

Broker

s Mortgag

e Banks

Investment

Banks

Savings

Funds

Commercial Paper Markets

Credit Markets

Bond Markets

RL

loans

RA RL

Invest


LEVERAGE


Securitization: mortgages collected into highly leveraged Special Purpose Vehicles with face value $0.5B ~ $1.5B

Source: Gorton, 2008

EQUITY 3%

BONDS97%

ASSET


Ratings Agencies assess probability of default

Ashcraft and Schuermann, 2008


Credit enhancement during home price appreciation (HPA) cycle



LOSSES


[Subprime Crisis] Extract equity from houses



High Leverage wipes out equity quickly


EQUITY 0%

BONDS97%

ASSET-3%


[Subprime Crisis] Bubble burst in late 2008

From http://seekingalpha.com/


LINKAGES


1. Network of FSEs with abstract dependency couplings

Model / predict viability dynamics

Ownership hierarchy

FSE1

FSE2

FSE3

FSE2

FSE3

FSE1

2. Network of FSEs with specified holder and guarantor dependency linkages (MBS)

Model /predict asset/liability flows

3. Network of FSEs with: specified holder and guarantor

dependency linkages (MBS) Underlying pool and payment

structureModel / predict underlying pool

cash flows ( aggregated data )

FSE1

FSE2

FSE3

Pool a

$

$

Pool b$

$

FSE1

FSE2

FSE3

Pool a

$

$

Pool b$

$

4. Network of FSEs with: specified holder and guarantor

dependency linkages (MBS) underlying pool and payment modelModel / predict individual mortgage

default, prepayment behaviors

FSE4

FSE4

FSE4

FSE4

Individual loans

FSE = Financial Services Entity

Networks within networks


Moral Hazard at every step of the way



Bank Y

AC

L

Bank X

A

C

L

Bank Z

A???

L

Bank V

AC

L

Bank W

AC

L

Bank U

AC

L

Probabilistic network and controlled queue models for predictive analysis for credit networks.

Estimate marginal contribution to systemic risk by specific balance sheet trends.

Linkages between FSE


LIQUIDITY

Huge quantities of liquid assets disappear.

Banks cannot intermediate, or make new loans

Economy switches to new equilibrium – hysteresis.


[Subprime Crisis] Debt ended up on the taxpayer’s books.


[Subprime Crisis] US Budget Deficit rising to WWII levels

2012 = 100% GDP

2012 = 14T USD


[Subprime Crisis] A new equilibrium

2012 – no change


Public Financial Stability

regulators and central banks

Private Financial Services

Businesses ( banks, etc )

Broad scope riskTechnology

Public Interest Private - Commercial

Goal: financial stability profit + broad risk avoidance

Data view: high level view of detailed view of this firms positions positions of all market estimated counterparty positions participants

Current evolving systemic scaled market, credit etc risk IT systems Risk IT risk analytics to operate business Capabilities: evolving broad scope risk capabilities

Market, credit etc risk Technology

Financial stability regulators need scaled data driven broad scope risk IT capabilities to understand stability of a complex financial system (of systems)

reporting

Stress test

Public and private entities both need broad scope risk analytics


SP Valuation – clues for monitoring and managing risk

00 minimize Z

ttt MZ

0 TTZ

Funding forecasted “worst case” may price you out of the market … unless all banks use the same forecasts.

Self-financing trades only possible if there is sufficient Liquidity

Risk of loss is modeled as “hard constraint”

MV , VRisk-neutral distribution performs “stochastic discounting” to present

Risk is only as good as forecast of “worst case”.

Forecasts need to accommodate macro-economic risks – STRESS TESTS

Market valuation may be VERY far from fair valuation

Implies bank is insolvent even though fair value is healthy


MARKET DEPTHPrice data, order book and execution data, and position data.

Descriptive:Clustering of positions held by major participants;Classification of transaction type based on volume, rate, and spreads;

Predictive:Buy/sell potential surface given price and volume movements over time.Transaction correlation landscape

Prescriptive:Optimal “liquidity put” valuation for treasuries and central banksLiquidity Value Adjustment reserve management

TRANSPARENCY and common STRESS-TEST VALUATIONTerm sheets of liquid securities; collateralized lending data; market depth data; counterparty network data

Descriptive:Asset response to economic and financial scenariosCollateralized lending price response given market depth

PredictiveFair value pricing of assets based on cash flow fundamentalsEconomic capital response given stress test and/or business scenarios

PredictiveMark to market valuation given counterparty, investor and market scenariosLiquidation valuation of market positions

COUNTERPARTY NETWORKSMarket data, position data and balance sheet data.

Descriptive:Graph counterparties and obligations;Anonymized distribution of counterparty data.

Predictive:Distribution of losses from stress scenarios.Impact of failures of market participants;

PrescriptiveCritical counterpart identificationCounterparty Value Adjustment reserves management

Systemic Risk Requirements – sharing and transparency


A complex society is composed of many interdependent sectors.

Systemic risk technology is a general approach to broad-scope analytics

Transparency Standardized data, composable models Near real-time feed processing

Federated Coherent distributed databases Multiple users – private instances

Insights Detect emerging risk trends Explore mitigation consequences

Economic Infrastructure– Resilience to disaster– Environmental processes– Development prospects

Historical Data Future ScenariosMitigation PotentialUnanticipated Consequences

Current DataTrends & Signals

Systemic Risk – Beyond Finance

Economic Development: – Political system– Education and Health– Military and Industrial capabilities

S1

Analytical Services

Stress Scenarios

Data Management

AnalyticsInterfaces

High PerformanceClouds

S2 Sn…

M1 M2 Mn…


Sources

White papers from SSRN (Social Sciences Research Network http://www/ssrn.com )

– G. B. Gorton, NBER: The Panic of 2007 (July 2008)

– A.B. Ashcraft and T. Schuermann, Fed. Res. Bank of NY: Understanding the Securitization of Subprime Mortgage Credit (March 2008)

– S. G. Ryan, NYU Stern School: Accounting in and for the Subprime Crisis (March 2008)

– M.G. Crouhy, R.A. Jarrow and S.M. Turnbull: The Subprime Credit Crisis of 07 (July 2008)

© 2012 IBM Corporation Black Swans – Systemic Risk in Finance Alan King IBM Thomas J. Watson...

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Transcript of © 2012 IBM Corporation Black Swans – Systemic Risk in Finance Alan King IBM Thomas J. Watson...