Expected default frequency

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1 KM Model

2 Expected default frequency Expected default frequency (EDF) is a forward-looking measure of actual probability of default. EDF is firm specific. KM model is based on the structural approach to calculate EDF (credit risk is driven by the firm value process). It is best when applied to publicly traded companies, where the value of equity is determined by the stock market. he market information contained in the firm s stock price and balance sheet are translated into an implied risk of default.

3 Distribution of terminal firm value at maturity of debt firm value σ exp µ + σ Z 0 0 par value default region time According to KM s empirical studies, log-asset returns confirm quite well to a normal distribution, and σ stays relatively constant.

4 hree steps to derive the actual probabilities of default: 1. Estimation of the market value and volatility of the firm s asset.. Calculation of the distance to default, an index measure of default risk. 3. Scaling of the distance to default to actual probabilities of default using a default database.

5 Estimation of firm value and volatility of firm value σ Usually, only the price of equity for most public firms is directly observable, and in some cases, part of the debt is directly traded. Using option pricing approach: equity value, E f(, σ, K, c, r) and volatility of equity, σ g(, σ E, K, c, r) where K denotes the leverage ratio in the capital structure, c is the average coupon paid on the long-term debt, r is the riskfree rate. Solve for and σ E from the above equations.

6 default point, d * short-term debt + long-term debt Distance to default 1 distance to default, ( ), ˆ ln ) ( ˆ * * 0 d E d d f σ µ σ σ + where 0 is the current market value of firm, µ is the expected net return on firm value and is the annualized firm value volatility. σˆ

7 Probabilities of default from the default distance EDF 40 bp default distance Based on historical information on a large sample of firms, for each time horizon, one can estimate the proportion of firms of a given default distance (say, d f 4.0) which actually defaulted after one year.

8 Example Current market value of assets 0 1,000 Net expected growth of assets per annum * µ 0% Expected asset value in one year 1,00 Annualized asset volatility σ 100 Default point d * 800 1, default distance, d f Among the population of all the firms with d f 4 at one point in time, say 5,000 firms, 0 defaulted in one year. hen 0 EDF1 - yr bp. 5,000 * KM Credit Monitor uses a constant asset growth assumption for all firms in the same market.

9 Example Federal Express (dollars in billions of US\$) November 1997 February 1998 Market capitalization \$7.9 \$7.3 (price shares outstanding) Book liabilities \$4.7 \$4.9 Market value of assets \$1.6 \$1. Asset volatility 15% 17% Default point \$3.4 \$3.5 Default distance EDF %(6 bp) AA %(11 bp) A he causes of changes for an EDF are due to variations in the stock price, debt level (leverage ratio), and asset volatility.

10 Key features in KM model 1. Dynamics of EDF comes mostly from the dynamics of the equity values.. Distance to default ratio determines the level of default risk. his key ratio compares the firm s net worth to its volatility. he net worth is based on values from the equity market, so it is both timely and superior estimate of the firm value. 3. Ability to adjust to the credit cycle and ability to quickly reflect any deterioration in credit quality. 4. Work best in highly efficient liquid market conditions.

11 Strength of KM approach Changes in EDF tend to anticipate at least one year earlier than the downgrading of the issuer by rating agencies like Moody s and S & P s. EDF provides a cardinal rather than ordinal ranking of credit quality.

12 Accurate and timely information from the equity market provides a continuous credit monitoring process that is difficult and expensive to duplicate using traditional credit analysis. Annual reviews and other traditional credit processes cannot maintain the same degree of vigilance that EDFs calculated on a monthly or a daily basis can provide.

13 Weaknesses of KM approach It requires some subjective estimation of the input parameters. It is difficult to construct theoretical EDF s without the assumption of normality of asset returns. Private firms EDFs can be calculated only by using some comparability analysis based on accounting data. It does not distinguish among different types of long-term bonds according to their seniority, collateral, covenants, or convertibility.

14 Example aluation of a zero coupon bond with a promised payment in a year of \$100, with a recovery of (1 LGD) upon issuer s default. * LGD loss given default (assumed to be 40% here) Q risk neutral probability that the issuer defaults in one year from now (assumed to be 0% here) he expectation is calculated using the risk neutral probabilities but not the actual probabilities as they can be observed in the market from historical data or EDFs.

15 1 Q no default default \$100 1 Q \$100(1 LGD) + 1 Q \$100 LGD Q \$100(1 LGD) Q \$100(1 LGD) Q 0 risky bond default free component risky component

16 P 1 P(risk-free cash flow) \$100 (1 LGD)/(1 + r) \$54.5 r risk free rate (assumed to be 10% here) P P (risky cash flow) E Q (discounted risky cash flow) 100LGD(1 Q) + 0 Q 1+ r Let s denote the credit spread \$ (1 LGD) 1+ r 100LGD(1 Q) + 1+ r r + s so that s LGD Q (1 + r) 1 LGD Q 9.6%.

17 Derivation of the risk neutral EDFs * Let be the firm value process at under the modified risk neutral process. * dt rdt +σdz * t where Q P [ r * Pr Z Pr ln 0 DP ln t + r d * ] σ [ ( ) ] 0 σ + r DP σ + σ Z ( ) r σ. 0 ln DP + σ ln DP N( d * )

18 On the other hand, the expected default frequency under the actual process is given by EDF N( d ) where Hence, the risk neutral EDF Q N N d ( ) µ σ. 0 ln DP + 1 σ ( EDF ) + µ r σ. From CAPM, we have ρ FMσ µ r β ( µ M r) ( µ M r) σ where β beta of the asset with the market portfolio µ M r market risk premium for one unit of beta risk (to be estimated by a separate statistical process). M

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