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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