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Measuring & Managing Credit Risk EDF Credit Measures for Public Firms October 2006.

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Presentation on theme: "Measuring & Managing Credit Risk EDF Credit Measures for Public Firms October 2006."— Presentation transcript:

1 Measuring & Managing Credit Risk EDF Credit Measures for Public Firms October 2006

2 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 2 Session Objectives 1. What is the EDF credit measure? 2. When does a firm Default? 3. What drives the EDF credit measure? 4. How are the EDF Drivers calculated? 5. How is the EDF measure calculated from the EDF Drivers? 6. EDF methodology summary 7. EDF validation 8. Conclusion

3 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 3 EDF Credit Measure for Public Firms EDF stands for Expected Default Frequency – the probability that a firm will default within a given time horizon by failing to make an interest or principal payment. We provide EDF measures for time horizons of 1, 2, 3, 4, and 5 years. EDF Ranges from.02% to 20%, i.e., 2 to 2000 basis points. Say we create a portfolio of 100 firms, each with a 2% EDF. On average, 2 out of the 100 firms will default over the next year, and 98 will not default. A firm with a 2% EDF is 10 times more likely to default than a firm with a 0.2% EDF

4 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 4 1-Year EDF S&P Rating Source: Credit Monitor Default Example: Collins & Aikman Corp 1-Year EDFS&P Rating Defaulted May The value of EDF: Measures credit risk in terms of absolute default probabilities rather than relative rankings. Provides the most accurate forward-looking, causal model. Provides frequent updates and early warning of changes in credit quality.

5 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 5 Agency Ratings: An Example

6 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 6 Agency Ratings: Another Example

7 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 7 Differentiating EDF Credit Measures from Traditional Ratings EDF Credit Measures Objective, Market driven Quantitative Method Quantitative Output EDF = 0.02% (An actual probability of default) Absolute (Cardinal) Precise and continuous, providing full granularity (high resolution) Specific time horizon Reflects current assessment of future prospects of the firm/economy Dynamic, updated daily or monthly Reflects issuers default probability (PD), and not issue-specific LGD Traditional Ratings Subjective, driven by fundamental analysis Qualitative Method Qualitative Output AAA = Obligors capacity to meet its financial commitment on the obligation is extremely strong. Relative (Ordinal) Distinct risk buckets without specifying or targeting a specific default rate No specific time horizon (long term) Supposed to reflect average economic conditions – through the cycle Stable (low ratings volatility) Opinion on Expected Loss – combines the effect of PD and LGD (Loss Given Default)

8 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 8 Default Example: Tropical Sportswear intl. 1-Year EDF Defaulted December 2004 S&P Rating Source: Credit Monitor 1-Year EDF S&P Rating

9 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 9 When does a firm Default? A firm defaults when the value of its business falls below what it owes. If the firm value is greater than what it owes, the equity holders have the ability and incentive to pay the debt obligations and keep the firm alive. A firms ability to pay its debt depends more on the Market Value of its Assets, and less on its cash position. If the assets of the firm have sufficient market value, the firm can raise cash by selling a portion of its assets - or by issuing additional equity or debt. EDF is the probability that a firms future market value will be insufficient to meet its future debt obligations

10 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 10 Source: Credit Monitor Market Value of Assets Default Point (Liabilities Due) Default Example: Tropical Sportswear Intl. Defaulted December 2004 Defaulted December 2004 EDF at T1 is greater than EDF at T2. Higher Leverage implies Higher EDF. Asset Value and Liabilities drive EDF. What else drives EDF? T2 ? T1 ? Market Value of AssetsDefault Point $ Million

11 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 11 Does similar Leverage imply similar EDF? Pepsis Market Value of Assets Dells Market Value of AssetsPepsis Default Point Dells Default Point Pepsis Market Value of Assets Dells Market Value of Assets Dells Default Point Pepsis Default Point February 2003: Dell and Pepsi had similar Market Leverage. Did they have similar EDF? $ Million Source: Credit Monitor

12 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 12 Pepsis EDF Dells EDF Why is Dells EDF substantially higher? Asset Volatility: 46% (Dell) vs. 19% (Pepsi) Asset Volatility and Leverage drive EDF Pepsi and Dell in Feb 2003: Similar Leverage but different EDF Source: Credit Monitor Pepsis EDFDells EDF

13 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 13 EDF Drivers 1. Market Value of Assets (or Business Value) Market assessment of the future cash flows of the business Value of the firm as a going concern 2. Default Point (or Liabilities Due) The liabilities due in the event of distress 3. Asset Volatility (or Business Risk) The variability of the market value of assets

14 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 14 sset Value EDF Drivers: A Pictorial View Distribution of Market Value of Assets at Horizon (1 Year) EDF Expected Market Value of Assets Asset olatility (1 Standard Deviation) efault Point D A V Today Time 1 Year Note: The symbol ~ stands for increasing function of or directionally equivalent to D A V Asset olatility efault Point ] log [ sset Value / EDF ~

15 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 15 Impact of the EDF Drivers on EDF Default Point goes up………………….….………….EDF goes Market Value of Assets goes up……….……………EDF goes DOWN Market Leverage (Default Point / Market Value of Assets) goes up………………………………………...….…...EDF goes Asset Volatility goes up………….…..….……………EDF goes UP D A V Asset olatility EDF is an increasing function of : efault Point ] log [ sset Value /

16 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 16 Estimating Market Value of Assets Market Value of Assets is the total market value of the firm as a going concern. Market Value of Assets is the Net Present Value of the firms future cash flows. Market Value of Assets is NOT the Book Value of Assets. Market Value of Assets is NOT the Market Capitalization, which is equal to the Market Value of Equity. Even though the Equity of a public firm is traded in the markets, the firms Assets are not traded. Market Value of Assets is not directly observable

17 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 17 Value Assets* A firm derives value from the cash flows it is expected to generate. For most firms, we cannot find Market Value of Assets by adding Market Value of Liabilities and Equity as the total amount of market value of liabilities is not available (most debt is not traded). Moreover, Market Value of liabilities is not equal to the Book Value of Liabilities, because the Market Value of Liabilities changes with credit quality. Estimating Market Value of Assets: Capital Structure of a Firm Liabilities* Senior claim on the Assets. Upside limited to principal and interest. Equity* Junior claim on the Assets. Unlimited upside and limited downside. * Asset, Liability, and Equity depicted above are market values and not book values. Claim Future Cash Flow produced by Assets Claim Liabilities and Equity represent a complete set of claims on the asset value

18 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 18 What is the relationship between Equity and Market Value of Assets? Equity holders have the right, but not the obligation, to buy the firms assets from the lender by re-paying the debt. When the Market Value of Assets is above what is owed, Equity holders can exercise the above right. As the Asset Value increases beyond what is owed, Equity Value continues to increase: Unlimited upside. When the Market Value of Assets is below what is owed, Equity holders can choose not to exercise the above right. As the Asset Value goes below what is owed, Equity Value approaches zero - but never goes negative: Limited downside (limited liability). Key insight from Black, Scholes, and Merton (1973,74): Equity is a Call Option on the firms Assets. Derivative Pricing Theory (Black, Scholes, etc.) provides a mathematical relationship between Market Value of Assets (Underlying) and Equity Value (Derivative)

19 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 19 Strike Price (Contractual Amount) Underlying Call Option Value Estimating Market Value of Assets: Equity as a Call Option on the Assets Liabilities (Contractual Amount) Market Value of Assets Equity Value Generic Call Option Equity as a Call Option on the firms Assets Strike PriceLiabilities UnderlyingMarket Value of Assets Call Option ValueEquity Value

20 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 20 Equity as a Call Option on the Assets Equity is a Call Option on the Market Value of Assets. Option Pricing Theory provides an extensively validated relationship between Equity Value and Market Value of Assets. Given an Equity Value, this relationship can be used to solve for the Market Value of Assets. Liabilities (Contractual Amount) Market Value of Assets Equity Value

21 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 21 MKMV Public EDF Model: An Extension of the Merton Model MertonMKMV Public EDF Model Two classes of Liabilities: Short Term Liabilities and Common Stocks Five Classes of Liabilities: Short Term and Long Term Liabilities, Common Stocks, Preferred Stocks, and Convertible Stocks No Cash PayoutsCash Payouts: Coupons and Dividends (Common and Preferred) Default occurs only at Horizon.Default can occur at or before Horizon. Equity is a call option on Assets, expiring at the Maturity of the Short Term Liabilities. Equity is a perpetual call option on Assets; it never expires

22 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 22 Default Point The amount of Liabilities due in the event of distress The threshold level of Market Value of Assets, below which the firm defaults Default Point is a function of the firms Liability structure, as described by the balance sheet - specifically the short/long term breakdown of Liabilities. MKMV empirical research: Default Point lies between the Short Term Liabilities and the Total Liabilities. Default Point Short Term Liabilities + ½ of Long Term Liabilities

23 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 23 Default Point Source: Credit Monitor Market Value of Assets Total Liabilities Defaulted December 2004 Defaulted December 2004 Default Example: Tropical Sportswear Intl. Market Value of Assets Default Point Total Liabilities $ Million

24 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 24 Asset Volatility A measure of the variability in the firms future Market Value of Assets. Reflects the degree of uncertainty in the firms future earnings. Quantifies business risk: Firms in the same industry/country and of similar size tend to have similar asset volatilities. Neither the Market Value of Assets, nor the Asset Volatility is directly observable. They can be implied from the Equity Value and Equity Volatility

25 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 25 Asset 100 Liability 80 Equity 20 Estimating Asset Volatility and the Role of Leverage Leverage Change in Equity = +100% (20/20) Change in Asset Value = +20% (20/100) Home originally worth:100 Down Payment:20 Bank Loan:80 Home now worth:120 De-Leverage 1/5 Change in Asset Value = +20% (20/100) Change in Equity = +100% (20/20) A measure of the variability in the firms future Market Value of Assets (business risk). Measured as the standard deviation of the Annual % Change in the Market Value of Assets. Example: Asset Volatility of 30% indicates that a typical change in the business value is plus or minus 30% over the next year. Volatility

26 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 26 Asset Return Equity Return Estimating Asset Volatility using the Options Framework The same Options Framework that links Equity Value to the Market Value of Assets also links Equity Volatility to Asset Volatility. Equity Returns are de-levered to obtain Asset Returns. Asset Returns are then used to calculate the Empirical Asset Volatility. The Empirical Asset Volatility (historical) gains predictive power when blended with a comparables-based (along size, profitability, country, industry) volatility measure (Modeled Asset Volatility). Empirical Asset Volatility Modeled Asset Volatility Asset Volatility ……… Standard Deviation

27 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 27 Estimating Asset Volatility Asset Volatility is NOT equal to Equity Volatility. Leverage amplifies the Volatility of the underlying Assets to produce a higher Volatility at the Equity level. The same Options Framework that links Equity Value to the Market Value of Assets also links Equity Volatility to Asset Volatility. Equity Returns are de-levered to obtain Asset Returns, which are then used to calculate an historical Asset Volatility. The historical Asset Volatility is combined with volatility information from comparable firms (of similar size, profitability, industry, country) to estimate each firms final Asset Volatility

28 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 28 0% 5% 10% 15% 20% 25% 30% 35% ,00010,00050,000100,000200,000 Total Assets ($m) Annualized Volatility COMPUTER SOFTWARE AEROSPACE & DEFENSE FOOD UTILITIES, ELECTRIC BANKS AND S&LS Asset Volatility: Measure of Business Risk

29 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 29 Time Rising Equity Market Cap Because of rising Asset Volatility But a Dramatically Higher EDF Asset Volatility: A Critical EDF Driver

30 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 30 Calculating EDF from EDF Drivers and Distance to Default Distance to Default (DD) The number of Standard Deviations the Market Value of Assets is away from Default Point Why is the relationship between EDF and the three drivers defined as above? Expected Default Frequency = Probability that the Market Value of Assets in 1 Year falls below the Default Point. = Solid Area under the Probability Curve. For example, N (-DD) is the Probability that a Standard Normal Variable will be DD or farther below the mean. (Normality was Mertons assumption, but is not used by MKMV) Asset Value Today Time Value EDF 1 Year Expected Market Value of Assets Asset Volatility (1 Standard Deviation) Default Point Asset Volatility EDF Default Point Market Value of Assets X

31 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 31 Calculating EDF from EDF Drivers and Distance to Default Distance to Default (DD) The number of Standard Deviations the Market Value of Assets is away from Default Point Asset Value Today Time Value EDF 1 Year Expected Market Value of Assets Asset Volatility (1 Standard Deviation) Default Point DD is the distance between the (log of) Market Value of Assets and Default Point, expressed as a multiple of Asset Volatility. EDF is an increasing function of 1/DD and a decreasing function of DD. Distance to Default (DD) log [ Market Value of Assets Default pointAsset Volatility 1 ] X

32 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 32 Distance to Default (DD) DD represents the cushion between Market Value of Assets and Default Point, expressed as a multiple of Asset Volatility. The Larger the DD, the Larger the cushion, and the Lower the EDF. The Smaller the DD, the Smaller the cushion, and the Higher the EDF. EDF moves inversely with DD. It provides a relative rank ordering of Default Risk. We must transform DD into EDF to get an absolute probability of default

33 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 33 Transforming DD to EDF: Classic Merton Approach The classic Merton approach uses Normality and other simplifying assumptions to map DD to EDF, using a Cumulative Normal Distribution transformation: Merton EDF = N(-DD), where N is the Cumulative Normal Distribution function. EDFs calculated using the above approach (i.e., simplifying assumptions) dramatically understate the default probability. Example: Firms with DD = 4 are predicted to have a default rate of 0.003% under the Normal distribution assumption. But, in reality, firms with DD = 4 are found to have a default rate of 0.6%: 200 times that predicted by Normality assumptions! Normality or other simplifying assumptions CANNOT be used in mapping DD to EDF

34 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 34 Realized PD Assumptions vs. Reality: Firms tend to change Liabilities as they approach distress. Firms tend to change (increase) Liabilities as they approach distress. Reality Merton PD Liabilities are assumed to be stable over time. Merton Model Default Point Increase in Liabilities can be proxied, say, by a higher Default Point, causing a higher PD. Horizon Asset Value Time Asset Value

35 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 35 Assumptions vs. Reality: Default can happen anytime before Horizon. Defaulting Path Default occurs the first time Asset Value falls below Default Point. Reality Merton PD Default Point Non-Defaulting Path ? Merton Model: Non-Defaulting Path Reality: Defaulting Path Realized PD Default occurs when Asset Value at Horizon is below Default Point. Merton Model Horizon Asset Value Time

36 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 36 Assumptions vs. Reality: Asset Return Distribution is Fat Tailed. Asset Return Distribution is Fat Tailed. Reality Asset Return Distribution is Normal. Merton Model Merton PD Realized PD Default Point Horizon Asset Value Time Asset Value

37 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 37 Modeling the Default Process is difficult. How do we overcome it? We cannot use the Normal Distribution to map DD to EDF. Reasons include: Firms change Liabilities as they approach distress; Default Point is not constant. Default can happen anytime before Horizon; Default Point is an absorbing boundary. Asset returns are fat-tailed relative to the Normal Distribution. The default processes of actual firms are difficult to model analytically. We overcome the above challenges by empirically mapping DD to EDF

38 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 38 Empirically mapping DD to EDF DD is empirically mapped to EDF by tracking the default experience of thousands of firms from MKMVs extensive Default Database. MKMV uses actual default rates for companies in similar risk ranges (DD buckets) to determine a one-to-one relationship between DD and EDF. Distance to Default EDF

39 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 39 MKMV Database MKMV Public Firm Default Database (Global) (7,600 + defaults) Defaults Quarter

40 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 40 Empirically mapping DD to EDF ) Search the Default Database for all instances of firms having a DD of 6. 2) 42,000 such instances (of firms having a DD of 6) were found. 3) 17 out of these 42,000 instances resulted in Default in the next 1 year. 4) Empirical Default Rate corresponding to a DD of 6 is 17/42000 =.04% = 4bp 5) Map DD of 6 to an EDF of.04% = 4bp 6) Repeat above steps for other DD buckets. DD = 6 42,000 Instances of DD = 6 17 Defaults over the next 1 year 1-Year EDF =17 / 42,000 = 4bp

41 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 41 Empirically mapping DD to EDF DD =1 DD = 4 DD = 2 DD = 5 DD = 3 DD = Instances Instances 20,000 Instances Instances40,000 Instances 42,000 Instances 720 Defaults 450 Defaults 200 Defaults 150 Defaults28 Defaults 17 Defaults EDF =800bp EDF =300bp EDF =100bp EDF =43bpEDF =7bp EDF =4bp

42 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 42 Distance to Default EDF EDF = 0.43% Calculating EDF using the DD to EDF Mapping EDF = 20% EDF = 0.02% DD =

43 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 43 DD to EDF: One-to-One Relationship Two firms with the same DD will have the same EDF - even if they differ with respect to Size, Industry, Geography, or the EDF Drivers. Size, Industry, and Geography do affect Default Risk. The effects of Size, Industry, and Geography are already embedded in DD via the three EDF Drivers: Market Value of Assets, Asset Volatility, and Default Point. Distance to Default EDF

44 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 44 EDF methodology summary Asset Volatility Market Value of Assets Default Point Distance to Default DD-EDF Mapping EDF Equity is a Call Option on the Assets. Solve for Market Value of Assets and Asset Volatility. Market Value of Equity Amount of Short and Long Term Liabilities Amount of Short/Long Term Liabilities determine Default Point Distance to Default is the cushion between Market Value of Assets and Default Point, expressed as a multiple of Asset Volatility. MKMVs Default Database is used to empirically map DD to EDF. EDF is the probability that the firm will default within the specified time horizon

45 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved Yr Asset Value 1-Year EDF Expected Market Value of Assets Today Time Value EDF Methodology: EDF for Horizon beyond 1 Year 2 Yrs Distribution of Market Value of Assets at Year 1 1-Year Distance-to-Default 2-Year Cumulative EDF Distribution of Market Value of Assets at Year 2 1-Year Asset Volatility 1-Year Default Point 2-Year Default Point 2-Year Asset Volatility 2-Year Distance-to-Default

46 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 46 Tropical Sportswear Intl.: EDF along with the three EDF Drivers 1-Year EDF Market Value of Assets Default Point Asset Volatility Defaulted December 2004 Asset Value ($ Million), Default Point ($ Million) EDF (%), Asset Volatility (%) Market Value of Assets Asset Volatility Default Point EDF

47 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 47 Early Warning Power of EDF Measures Months Before and After Default EDF, Rating MKMV EDF Moodys Rating Median EDF and Rating-Implied EDF for Defaulted Firms United States Data: Median EDF tends to start rising 24 months before default. Median Rating tends to stay flat until a year before default, showing a steep rise about 4 months before default. EDF tends to lead the Ratings. EDF provides early warning power. EDF is dynamic and continuous, while Ratings move in discrete steps

48 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 48 Does the Predicted Default Rate (EDF) match the Actual Default Rate? Predicted and Actual Number of Defaults US Public Non-Financial Firms w/ Sales > 300 M Years EDF < 20%

49 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 49 Does the Predicted Default Rate (EDF) match the Actual Default Rate? EDF = 20% Predicted and Actual Number of Defaults US Public Non-Financial Firms w/ Sales > 300 M Years

50 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 50 Do EDF Measures provide Discriminatory Power over that provided by Agency Ratings? Study Performed by 3rd Party on behalf of prospective client, published in Risk Magazine 103 Non-Financial Single B firms on 12/31/92 were sorted by their EDF, as shown on the next slide. Observed wide range of risk (as measured by EDFs) from 0.15% to 20%

51 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved B-Rated Firms as of 31/12/92 Number of Firms EDF Range High RiskLow Risk

52 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 52 Defaults 6 Months Later Number of Firms EDF Range High RiskLow Risk

53 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 53 Defaults 1 Year Later Number of Firms EDF Range High RiskLow Risk

54 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 54 Defaults 2 Years Later Number of Firms EDF Range High RiskLow Risk

55 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 55 Defaults 3 Years Later Number of Firms EDF Range High RiskLow Risk

56 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 56 Defaults 4 Years Later Number of Firms EDF Range High RiskLow Risk Equal rated firms do not have the same risk EDF adds significant discriminatory power

57 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 57 Benefits of the EDF Credit Measure Evaluate Default Risk with a greater degree of accuracy and objectivity. Quantify Default Risk, to: 1. Price appropriately 2. Improve portfolio performance. Focus resources where they add the most value. EDF is updated frequently and provides early warning of changes in credit quality. Cause-and-Effect model facilitates What If and Pro-Forma Analysis

58 Measuring & Managing Credit Risk - EDF Credit Measures Copyright © 2006 Moodys KMV Company. All Rights Reserved. 58 Summary 1. What is the EDF credit measure? Forward-looking Probability of Default over a defined time horizon 2. When does a firm Default? Market Value of Assets < Default Point 3. What drives the EDF credit measure? Market Value of Assets, Default Point, and Asset Volatility 4. How are the EDF Drivers calculated? 5. How is the EDF measure calculated from the EDF Drivers? Distance to Default is empirically mapped to EDF via the Default Database 6. EDF methodology summary 7. EDF validation The EDF measure is accurate and powerful; it provides significant early warning. 8. Conclusion


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