Download Systemic Risk and Big Data

The National Institute of Finance
Did they know what was going on?
Did they have a choice?
Exposed to Lehman Brothers –
Owned $785M Lehman bonds
$2 Trillion Market
‘Broke the Buck’
Share price cut to 97¢
Exposed to Lehman Brothers –
Owned $785M Lehman bonds
$2 Trillion Market
‘Broke the Buck’
Share price cut to 97¢
“If this crisis has taught us anything, it has taught us that risk to our
system can come from almost any quarter. We must be able to
look in every corner and across the horizon for dangers and our
system was not able to do that.” Secretary Geithner, opening
remarks, testimony to Senate Banking Committee, June 18, 2009.
 New
equilibrium below full production
 Disruption
 Punishing
of access to credit
the system, not the firm
$3T CDS Notional outstanding
$3T CDS Notional outstanding
AIG goes down, who else goes?
$185b Gov. Loans
Portfolio insurance, trading strategy – Mimic put option, sell stock in decline
Rating Agency Arbitrage + Mortgage market decline = Toxic Assets
 Stressed
firms sell assets (adjust balance sheet)
 Contagion
I - Market (Liquidity) failure, firms
sell new assets and stress new markets
 Contagion
II - Market panic and there are runs
on markets
 Hysteresis
- Long term freezing of markets
What is the correlation?
 Hunt
Brothers ‘cornered’ Silver market
• 1980 controlled 1/3 of world silver
• Family fortune of $5b
• Margin requirements were changed
• Price dropped 50% on March 27, 1980
$21.62
$10.80
 Hunt
Brothers ‘cornered’ Silver market
• What else did the Hunt Brother’s own?
 Fire sale in silver
 Liquidity dried up
 Fire sale in cattle
 Correlations driven by?
 The
Russian Financial Crisis
• 1998 Long Term Capital Management
 Small exposure to Russian debt
 Large leveraged exposure to Danish debt
 The
Russian Financial Crisis
• 1998 Long Term Capital Management
 Russia defaulted 1998
 Holders of Danish debt hit by default
 Fire sale in Danish debt
 LTCM connected to everyone
 The
Credit Crisis
• Citibank’s exposure to CP Market?
 The
Credit Crisis
• Citibank’s exposure to CP Market?
 Off balance sheet, obligation to provide short term
financing to SIVs Citi had invented and formed.
 The
Credit Crisis
• Citibank’s exposure to MBS Market?
 SIVs bankruptcy remote – have to sell when in trouble
and what did they own – MBS ‘Toxic Assets’
Confidence in the market is gone, no-one knows who is solvent
Flight to quality (US Treasury), credit markets freeze
How long can companies ‘hold their breath’?
Experiment
Repeat
Experiment
The Economy is ‘path dependent’
 Predicting
Fire Sales
• Leverage of System
• Liquidity Capacity
 Velocity, depth of trading
 Capacity for bargain hunting
• Linkages – Book Correlation
 Loss
Distribution
• Tail events are rare – very little data
• Typically strong model assumptions
 Loss
Distribution
• Tail events are rare – very little data
• Typically strong model assumptions
• Liquidity Failures
 Can’t hedge
 No replicating portfolios
 Mean & Variance
 Game Theory
We are not in Kansas anymore
 Loss
Distribution
• Tail events are rare – very little data
• Typically strong model assumptions
• Liquidity Failures
• Scenario Analysis
 Linkages – rights, obligations
(Not Netting!)
 Granular Macro Economics
 Understanding Domino Risk
 Loss
Distribution
• Tail events are rare – very little data
• Typically strong model assumptions
• Liquidity Failures
• Scenario Analysis
• Economic Impact
 CaR – Credit at Risk
 DoL – Distribution of Loss
 Monitoring
health of Economy
 Regime shifting models
 Summaries reflecting stress
 Historical data
 Derivatives data
 Looking
for Black Swans
 Leverage measures
 Concentrations, Bubbles
 Liquidity capacity
 Linkages and Transparency
 Not
predicting cascading failures
 Determine
loss by counterparty
 Do
not predict probability of failure of
counterparties
 Do
not account for Linkages
 Legal
authorities must be strengthened
 Regulators
must understand the network
 Regulators
must understand aggregation
 Regulators
are ‘outgunned’
 Exchanges
and Clearing Houses
• Increase Liquidity
• Concentrate Risk
 System
views with existing resources
• Historical Data
• Market Data
• Firm Risk Systems
 Data Transparency
• Reference Data –
 Legal entities
 Product descriptions (Prospectus, Cash-flows, …)
 Details that ‘fit into’ a model
• Transactions/Price Data –
 Exchange, Clearing House, OTC (like TRACE)
 Position (Trading Book) data
 Essential for calibrating models
 Model Transparency
• Price a complex OTC
(How many can price?)
 Model Transparency
• Getting a 2nd opinion
 Model Transparency
• Getting a 2nd opinion
• Building an active research community
 Banks are not doing long-term research
 Regulators have limited efforts
 Academics have hard time getting data and funding
 Model Transparency
• Getting a 2nd opinion
• Building an active research community
• Current research efforts are incomplete
 Models under stress/Transition to new equilibrium
 Are markets complete (hedge-able)?
 National Weather Service equivalent?
 Competitive modeling environment (multiple
Hurricane models).
 Proposed
 Part
by concerned citizens
of regulatory reform legislation
 Collect
system-wide transaction data
 Develop
analytic tools
 Part
of the Federal Government
 Protect
data at highest level of security
 Metrics
to monitor risk – early warning
 NTBS
– post-mortum investigations
 Is
it feasible?
 Market
participants are close
Internal Systems (All Obligation)
-Reference Data
-Reporting Language
Prototypes
-Reference Data
-Systemic Model (MBS)
Hedge Fund Risks
-Hedge Fund Counter-party network
-Hedge Fund wide risk assessment
www.ce-nif.org