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 ‘Broke the Buck’ Share price cut to 97¢ $2 Trillion Market

Exposed to Lehman Brothers – Owned $785M Lehman bonds ‘Broke the Buck’ Share price cut to 97¢ $2 Trillion Market

“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 of access to credit  Punishing the system, not the firm

$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’?

The Economy is ‘path dependent’ Experiment Repeat Experiment

 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 2 nd opinion

 Model Transparency Getting a 2 nd 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 2 nd 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 by concerned citizens  Part 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 Prototypes -Reference Data -Systemic Model (MBS) Internal Systems (All Obligation) -Reference Data -Reporting Language Hedge Fund Risks -Hedge Fund Counter-party network -Hedge Fund wide risk assessment