Black Swans – Systemic Risk in Finance
Alan King
IBM Thomas J. Watson Research Center
joint with Francis Parr, IBM Research
© 2012 IBM Corporation
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 BC
Roman author, orator,
and politician (106 BC - 43 BC)
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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?
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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
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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:
 Lt
– future inflows:
 Kt
– net flows:
M t  Kt  Lt
}


E  M t  : Q, M
 t

Q
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Generic Valuation Operators
Fair Value
Default
P= 0.001
P, M :
paid
P= 0.949
Time
Fair value calibrates to historical
performance of similar flows.
Default
P= 0.05
Mark-to-Market
Q, M :
Calibrates to swap market:
interest rate normally distributed.
Q
Spreads correspond to default risk;
mark-to-market of spreads is valued in
Credit Default Swap (CDS) market.
Value
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Stochastic Programming Valuation*
1. Combines fair value and mark-to-market
2. Consistent with options pricing – risk-neutral valuation of Cox & Ross
Data:
State:
Action:
prices of n securities
positions in n securities
trades
Zt : Zt1 Ztn 
t : t1 tn 
t : t  t 1
3. Dual stochastic linear programs
(P) min
Z 0  0
subject to:
Zt  t  M t
withZT T  0
(D) max
V,M
subject to:
Zt  EV Zt 1 Zt  withV  0
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
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SP Valuation – interpretation of components
Self-financing trades
replicate flows over all
“states of world”
Zt  t  M t
Risk-neutral distribution
performs “stochastic
discounting” to present
Risk of loss is
modeled as “hard
constraint”
V,M
ZT T  0
V
Calibration to market by
incorporating “constraints”
“Risk neutral” valuation –
“real probabilities” not required.
Other risk models possible … dual
objective is “distance to real distribution”
minimize Z0  0
Minimize price – funds the “worst case” state of world
© 2012 IBM Corporation
1950’s – Good Bank “Hold to Maturity”
Earnings per share:
Federal Reserve Bank
(A*RA – L*RL)/C
EPS
Money
Supply
Reserves
(A – L) =
Capital
Assets
Liabilities
RA
households
households
deposits
loans
RL
Bank
Investors
Asset Insurance
Deposit Insurance
FNMA, FMAC, GNMA
FDIC
Savers
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1950’s – BAD Bank “on life support”
Earnings per share:
Federal Reserve Bank
Reserves
BAD
EPS
$$$
(A*RA – L*RL)/C
(A – L) =
BUST
Assets
Liabilities
RA
RL
Bank
$$$
$$$
Investors
households
households
deposits
loans
Asset Insurance
Deposit Insurance
FNMA, FMAC, GNMA
FDIC
Savers
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2000’s – New Banking: “Originate to Distribute”
Credit
Markets
Bank
Investment
Banks
Mortgage
Banks
Brokers
Investors
Invest
households
A-L =
Savings
Rated Bonds and Notes Funds
Collateralized
Debt
Obligations
Asset Backed
Securities
RA
Bundled Loans
households
loans
Commercial Paper
Markets
Bond Markets
RL
Savers
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LEVERAGE
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Securitization: mortgages collected into highly leveraged
Special Purpose Vehicles with face value $0.5B ~ $1.5B
ASSET
BONDS
97%
EQUITY 3%
Source: Gorton, 2008
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Ratings Agencies assess probability of default
Ashcraft and Schuermann, 2008
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Credit enhancement during home price appreciation (HPA)
cycle
Ashcraft and Schuermann, 2008
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LOSSES
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[Subprime Crisis] Extract equity from houses
Source: Gorton, 2008
© 2012 IBM Corporation
High Leverage wipes out equity quickly
ASSET
-3%
BONDS
97%
EQUITY 0%
Source: Gorton, 2008
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[Subprime Crisis] Bubble burst in late 2008
From http://seekingalpha.com/
© 2012 IBM Corporation
LINKAGES
© 2012 IBM Corporation
Networks within networks
FSE = Financial Services Entity
FSE 2
1. Network of FSEs with
abstract dependency
couplings
Model / predict viability
dynamics
Ownership hierarchy
4. Network of FSEs with:
specified holder and guarantor
dependency linkages (MBS)
FSE 1
FSE 3
FSE 4
underlying pool and payment model
Model / predict individual mortgage
default, prepayment behaviors
FSE 2
FSE 1
FSE 2
2. Network of FSEs with
specified holder and guarantor
dependency linkages (MBS)
Model /predict asset/liability flows
FSE 3
FSE 4
FSE 1
$
FSE 3
Pool b
Pool a
$
$
FSE 2
FSE 1
FSE 3
$
3. Network of FSEs with:
specified holder and guarantor
dependency linkages (MBS)
Underlying pool and payment
structure
Model / predict underlying pool
cash flows ( aggregated data )
FSE 4
Individual loans
Pool b
Pool a
$
FSE 4
$
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Moral Hazard at every step of the way
Ashcraft and Schuermann, 2008
© 2012 IBM Corporation
Linkages between FSE
Bank W
Bank U
C
A
C
A
L
L
Bank X
C
Bank V
A
L
C
A
L
Bank Y
Bank Z
C
A
L
???
A
L
Probabilistic network and controlled queue models for predictive analysis for credit networks.
Estimate marginal contribution to systemic risk by specific balance sheet trends.
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LIQUIDITY
Huge quantities of liquid assets disappear.
Banks cannot intermediate, or make new loans
Economy switches to new equilibrium – hysteresis.
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[Subprime Crisis] Debt ended up on the taxpayer’s books.
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[Subprime Crisis] US Budget Deficit rising to WWII levels
2012 = 14T USD
2012 = 100% GDP
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[Subprime Crisis] A new equilibrium
2012 – no change
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Public and private entities both need broad scope risk analytics
Public
Financial Stability
regulators and
central banks
reporting
Stress test
Broad scope risk
Technology
Public Interest
Goal:
financial stability
Private
Financial Services
Businesses
( banks, etc )
Market, credit etc risk
Technology
Private - Commercial
profit + broad risk avoidance
Data view: high level view of
positions of all market
participants
detailed view of this firms positions
estimated counterparty positions
Current
evolving systemic
Risk IT
risk analytics
Capabilities:
scaled market, credit etc risk IT systems
to operate business
evolving broad scope risk capabilities
Financial stability regulators need scaled data driven broad scope risk IT capabilities to
understand stability of a complex financial system (of systems)
© 2012 IBM Corporation
SP Valuation – clues for monitoring and managing risk
Self-financing trades
only possible if there
is sufficient Liquidity
Zt  t  M t
Risk-neutral distribution
performs “stochastic
discounting” to present
V,M
V
Market valuation may be
VERY far from fair valuation
Implies bank is insolvent
even though fair value is
healthy
Risk of loss is
modeled as “hard
constraint”
ZT T  0
Risk is only as good as forecast of “worst
case”.
Forecasts need to accommodate macroeconomic risks – STRESS TESTS
minimize Z0  0
Funding forecasted “worst case” may price you out of
the market … unless all banks use the same forecasts.
© 2012 IBM Corporation
Systemic Risk Requirements – sharing and transparency
MARKET DEPTH
Price 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 banks
Liquidity Value Adjustment reserve management
COUNTERPARTY NETWORKS
Market 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;
Prescriptive
Critical counterpart identification
Counterparty Value Adjustment reserves management
TRANSPARENCY and common STRESS-TEST VALUATION
Term sheets of liquid securities; collateralized lending data; market depth data; counterparty network data
Descriptive:
Asset response to economic and financial scenarios
Collateralized lending price response given market depth
Predictive
Fair value pricing of assets based on cash flow fundamentals
Economic capital response given stress test and/or business scenarios
Predictive
Mark to market valuation given counterparty, investor and market scenarios
Liquidation valuation of market positions
© 2012 IBM Corporation
Systemic Risk – Beyond Finance
A complex society is composed of many
interdependent sectors.
Historical Data
Current Data
Trends & Signals
Future Scenarios
Mitigation Potential
Unanticipated Consequences
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
S1
Data
Management
Stress Scenarios
…
S2
Sn
Analytics
Interfaces
High Performance
Clouds
M1
M2
…
Mn
Analytical Services
Economic Infrastructure
– Resilience to disaster
– Environmental processes
– Development prospects
Economic Development:
– Political system
– Education and Health
– Military and Industrial capabilities
© 2012 IBM Corporation
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)
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