Brian D. Gordon, Director Brian D. Gordon, Director

Overview of Presentation  Fitch is working on developing a completely new methodology for evaluating and rating CDOs  The criteria is still a work in progress and subject to substantial change prior to release  This presentation will give a “sneak peak” of the underlying approach, logic and application  Fitch is working on developing a completely new methodology for evaluating and rating CDOs  The criteria is still a work in progress and subject to substantial change prior to release  This presentation will give a “sneak peak” of the underlying approach, logic and application

The World in 1997  1997 was the first year that saw a substantial issuance of CDOs  CDO rating methodologies created at about the same time by all three agencies  The core of all three methodologies is basically unchanged from that time  1997 was the first year that saw a substantial issuance of CDOs  CDO rating methodologies created at about the same time by all three agencies  The core of all three methodologies is basically unchanged from that time

But the world has changed substantially since then …  Asian crisis in 1997  Russian default plus Long Term Cap Mgmt 1998  Bubble Economy  Record High Yield Default Rates  CDO new issuance volume reaches $85 billion in 2002  Market volatility increases dramatically  Asian crisis in 1997  Russian default plus Long Term Cap Mgmt 1998  Bubble Economy  Record High Yield Default Rates  CDO new issuance volume reaches $85 billion in 2002  Market volatility increases dramatically

U.S. High Yield Default Index Default VolumeDefault Rate The 2001 default rate excluding fallen angels was 9.7% The 2002 default rate excluding fallen angels was 12.4% Default Rates Default Volume (000's)

It’s Time for a Fresh Look at CDOs  CDOs are among the most innovative and complex financial structures in existence  CDO types include cash, synthetic, market value, high yield, high grade, trust preferred, etc.  They are self-contained portfolios of credit risk  The same construct that is applied to CDOs can be applied to any portfolio of credit risk, including ABCP, SIVs, bank and insurance portfolios  CDOs are among the most innovative and complex financial structures in existence  CDO types include cash, synthetic, market value, high yield, high grade, trust preferred, etc.  They are self-contained portfolios of credit risk  The same construct that is applied to CDOs can be applied to any portfolio of credit risk, including ABCP, SIVs, bank and insurance portfolios

The Major Drivers of Risk in CDOs  Default rates of underlying assets  Recovery rates of underlying assets  Structural considerations  Interest rate risk, FX risk  Management Risk, Moral Hazard  Execution and ramp-up risk  Default rates of underlying assets  Recovery rates of underlying assets  Structural considerations  Interest rate risk, FX risk  Management Risk, Moral Hazard  Execution and ramp-up risk

Drivers of Asset Default Rates  Rating of the underlying assets  Expected Life of the Assets  Correlation among the assets  Rating of the underlying assets  Expected Life of the Assets  Correlation among the assets

Measuring Asset Default Risk  Fitch will introduce an entirely new Default Matrix  Based on empirical default rate evidence from all three agencies  30 year cohort analysis  Establishes “base case” default expectations by rating (AAA to B) and life (1 to 10 years)  Fitch will introduce an entirely new Default Matrix  Based on empirical default rate evidence from all three agencies  30 year cohort analysis  Establishes “base case” default expectations by rating (AAA to B) and life (1 to 10 years)

Cumulative Gross Default Rates

Marginal Gross Default Rates

Total = $78.2bn Total = $109.8bn Telecommunication 36% Banking & Finance 13% Other 11% Metals & Mining 3% Transportation 2% Food, Beverage & Tobacco 2% Leisure & Entertainment 2% Chemicals 3% Paper & Forest Products 2% Automotive 5% Industrial/ Manufacturing 2% Utilities 19% Why Does Correlation Matter?

 The degree to which two series of variables move in unison What is Correlation? (1)

 The degree to which the default probabilities of two firms move in unison  A “structural model” model of default, based on the Black-Scholes option pricing model  The degree to which the default probabilities of two firms move in unison  A “structural model” model of default, based on the Black-Scholes option pricing model What is Default Correlation? (2) (3) (4)

The Correlation Matrix  25 Fitch defined industries  All companies within an industry similarly correlated  Inter-industry correlation is pair-wise correlation among industries (e.g. Chemicals to Auto)  Intra-industry correlation is the correlation within an industry (e.g. Chemicals to Chemicals)  Correlation for each company expressed as “Sector Average Security” which is a multiple regression across all other industries plus epsilon, a random variable representing unsystematic risk  25 Fitch defined industries  All companies within an industry similarly correlated  Inter-industry correlation is pair-wise correlation among industries (e.g. Chemicals to Auto)  Intra-industry correlation is the correlation within an industry (e.g. Chemicals to Chemicals)  Correlation for each company expressed as “Sector Average Security” which is a multiple regression across all other industries plus epsilon, a random variable representing unsystematic risk

Recovery Rates Recovery rates are a function of four variables 1)Systematic risk, implying that recoveries are inversely correlated to default rates 2)Idiosyncratic risk, meaning the unique properties of that company 3)The position of the debt in the capital structure of the company 4)The industry of the company Recovery rates are a function of four variables 1)Systematic risk, implying that recoveries are inversely correlated to default rates 2)Idiosyncratic risk, meaning the unique properties of that company 3)The position of the debt in the capital structure of the company 4)The industry of the company

Fitch CDO Recovery Rate Matrix  Fitch introduces the concept of tiered recovery rates, where the recovery rate varies with the stress scenario  For example, US Senior secured bank loans BBBBBBAAAAAA Recovery65%63%60%55%50%45%  Recoveries will also be time lagged for cash deals, but not for synthetics because of immediate valuation procedures  Fitch introduces the concept of tiered recovery rates, where the recovery rate varies with the stress scenario  For example, US Senior secured bank loans BBBBBBAAAAAA Recovery65%63%60%55%50%45%  Recoveries will also be time lagged for cash deals, but not for synthetics because of immediate valuation procedures

CDO Modeling  The Monte Carlo Model Generates a vector of defaults and recoveries that are required for each rating level  The Cash Flow Model Generates payment streams to rated liabilities using the payment waterfall and liability structure  The Monte Carlo Model Generates a vector of defaults and recoveries that are required for each rating level  The Cash Flow Model Generates payment streams to rated liabilities using the payment waterfall and liability structure

Monte Carlo Simulation  Uses a very large number of trial values for one or more random variables to produce a probability density function  The “brute force” method to solving differential equations  Used in the default generation model to produce inputs into the Cash Flow Model  May be applied to the Cash Flow Model in the future as well  Uses a very large number of trial values for one or more random variables to produce a probability density function  The “brute force” method to solving differential equations  Used in the default generation model to produce inputs into the Cash Flow Model  May be applied to the Cash Flow Model in the future as well

Monte Carlo Simulation Rating level default probability = D i Random number generator = V i p i = Pr(V i ≤ D i ) The degree in which two “random” variables are truly random, or conversely, move in unison, is dictated by the correlation assumption Rating level default probability = D i Random number generator = V i p i = Pr(V i ≤ D i ) The degree in which two “random” variables are truly random, or conversely, move in unison, is dictated by the correlation assumption

The Impact of Correlation on Default Distribution

Default Distribution 99.5% C.I. C. G. D. R. = 40% “AAA” 99.5% C.I. C. G. D. R. = 40% “AAA” 96% C.I. C. G. D. R. = 23% “BBB” 96% C.I. C. G. D. R. = 23% “BBB” N = 100 P = 10% (i = I, …, N) Number of Defaults

Summary of the New Approach  Draws upon empirical evidence for underlying assumptions about defaults, recoveries and correlation  Employs a rigorous mathematical approach  Uses state of the art modeling techniques, including Monte Carlo simulations  Widely applicable to all types of CDOs plus other credit dependent portfolios  Draws upon empirical evidence for underlying assumptions about defaults, recoveries and correlation  Employs a rigorous mathematical approach  Uses state of the art modeling techniques, including Monte Carlo simulations  Widely applicable to all types of CDOs plus other credit dependent portfolios

Roll Out and Impact  Expected release late Spring 2003  Immediate implementation after release  Likely to be more conservative than existing criteria  Fitch will release an article on the application of the new criteria to new and existing deals  Same methodology will be applied in Europe  Expected release late Spring 2003  Immediate implementation after release  Likely to be more conservative than existing criteria  Fitch will release an article on the application of the new criteria to new and existing deals  Same methodology will be applied in Europe