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Understanding Credit Risk Analysis: Ratings, Recovery Rates, and Default Probabilities

Learn about credit ratings, historical data, recovery rates, and estimating default probabilities in credit risk analysis. Explore using equity prices to estimate default probabilities and comparing real-world vs. risk-neutral default probabilities to enhance your financial knowledge.

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Understanding Credit Risk Analysis: Ratings, Recovery Rates, and Default Probabilities

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  1. Chapter 22Credit Risk 資管所 陳竑廷

  2. Agenda 22.1Credit Ratings 22.2Historical Data 22.3Recovery Rate 22.4Estimating Default Probabilities from bond price 22.5Comparison of Default Probability estimates 22.6Using equity price to estimate Default Probabilities

  3. Credit Risk • Arise from the probability that borrowers and counterparties in derivatives transactions may default.

  4. 22.1 Credit Ratings • S&P • AAA , AA, A, BBB, BB, B, CCC, CC, C • Moody • Aaa, Aa, A, Baa, Ba, B, Caa, Ca, C • Investment grade • Bonds with ratings of BBB (or Baa) and above best worst

  5. 22.2 Historical Data • For a company that starts with a good credit rating default probabilities tend to increase with time • For a company that starts with a poor credit rating default probabilities tend to decrease with time

  6. Default Intensity • The unconditional default probability • the probability of default for a certain time period as seen at time zero 39.717 - 30.494 = 9.223% • The default intensity (hazard rate) • the probability of default for a certain time period conditional on no earlier default 100 – 30.494 = 69.506% 0.09223 / 0.69506 = 13.27%

  7. Q(t) : the probability of default by time t (22.1)

  8. 22.3 Recovery Rate • Defined as the price of the bond immediately after default as a percent of its face value • Moody found the following relationship fitting the data: Recovery rate = 59.1% – 8.356 x Default rate • Significantly negatively correlated with default rates

  9. Source : • Corporate Default and Recovery Rates, 1920-2006

  10. 22.4 Estimating Default Probabilities • Assumption • The only reason that a corporate bond sells for less than a similar risk-free bond is the possibility of default • In practice the price of a corporate bond is affected by its liquidity.

  11. (22.1)

  12. 1 λ 1-λ 1 R λ 1-λ 1 Taylor expansion

  13. A more exact calculation • Suppose that Face value = $100 , Coupon = 6% per annum , Last for 5 years • Corporate bond • Yield : 7% per annum → $95.34 • Risk-free bond • Yield : 5% per annum → $104.094 • The expected loss = 104.094 – 95.34 = $ 8.75

  14. Q : the probability of default per year 288.48Q = 8.75 Q = 3.03% 0 1 2 3 4 5 e -0.05 *3.5

  15. 22.5 Comparison of default probability estimates • The default probabilities estimated from historical data are much less than those derived from bond prices

  16. Historical default intensity The probability of the bond surviving for T years is (22.1)

  17. Default intensity from bonds • A-rated bonds , Merrill Lynch 1996/12 – 2007/10 • The average yield was 5.993% • The average risk-free rate was 5.289% • The recovery rate is 40% (22.2)

  18. 0.11*(1-0.4)=0.066

  19. Real World vs. Risk Neutral Default Probabilities • Risk-neutral default probabilities • implied from bond yields • Value credit derivatives or estimate the impact of default risk on the pricing of instruments • Real-world default probabilities • implied from historical data • Calculate credit VaR and scenario analysis

  20. 22.6 Using equity prices to estimate default probability • Unfortunately , credit ratings are revised relatively infrequently. • The equity prices can provide more up-to-date information

  21. Merton’s Model If VT < D , ET = 0 ( default ) If VT > D , ET = VT - D

  22. V0 And σ0 can’t be directly observable. • But if the company is publicly traded , we can observe E0.

  23. Merton’s model gives the value firm’s equity at time T as So we regard ET as a function of VT We write Other term without dW(t) , so ignore it

  24. Replace dE , dV by (*) (**) respectively We compare the left hand side of the equation above with that of the right hand side (22.4)

  25. Example • Suppose that E0 = 3 (million) r = 0.05 D = 10 σE = 0.80 T = 1 Solving then getV0 = 12.40 σ0 = 0.2123 N(-d2) = 12.7%

  26. Solving

  27. Excel Solver F(x,y) =A2*NORMSDIST((LN(A2/10)+(0.05+B2*B2/2))/B2) -10*EXP(-0.05)*NORMSDIST((LN(A2/10)+(0.05+B2*B2/2))/B2-B2) [F(x,y)]2+[G(x,y)]2 =(D2)^2+(E2)^2 G(x,y) =NORMSDIST((LN(A7/10)+(0.05+B7*B7/2))/B7)*A7*B7

  28. Initial V0 = 12.40 , σ0 = 0.2123 • Initial V0 = 10 , σ0 = 0.1

  29. Thank you

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