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Class 4 Credit risk

Class 4 Credit risk. ‘ Neither a borrower nor a lender be ’ Polonius ( W.Shakespeare’s Hamlet). Why is credit risk so important? . Savings&loans (S&L) crisis in the US in 1980s The restructuring eventually cost the government $153 bln Defaults on emerging markets’ country debt

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Class 4 Credit risk

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  1. Class 4Credit risk ‘Neither a borrower nor a lender be’Polonius (W.Shakespeare’s Hamlet)

  2. Why is credit risk so important? • Savings&loans (S&L) crisis in the US in 1980s • The restructuring eventually cost the government $153 bln • Defaults on emerging markets’ country debt • Restructuring of LAm countries’ debt to Brady bonds in 1980s • Defaults on corporate bonds • Esp. junk bonds at the end of 1980s • Accumulation of bad quality bank loans in Japan, China,… • The 2008 global financial crisis • Total losses exceeded $1 trln • The second wave: crisis of the (European) sovereign debt • E.g., Greece restructured its gvt debt writing down €110 bln

  3. What is special about credit risk (compared to market risk) • Asymmetric payoff structure • Possibility of big losses vs. limited upside • Hard to measure → longer horizon (usually, annual) • Rare occurrence • Most loans are non-tradable • Esp. hard to model correlations • Discrete losses → limits at the transaction level • Interaction with market risk • Especially important for banks • Larger provisions under Basel: 4*VaR

  4. Credit risk vs. market risk

  5. How to model credit losses for a single instrument? Credit loss = D*LGD*СE • D: default indicator, equal to 1 with PD (probability of default) • PD is usually measured by credit rating • Loss given default (LGD): how many percent you lose in case of default • Recovery rate: RR=1–LGD • Credit exposure (CE): the amount we lose in case of default with zero recovery • Only the positive economic value counts: when someone owes you What is the formula for the expected losses?

  6. How to model credit losses for a portfolio? • The portfolio credit loss: CL = Σi[Di*LGDi*EADi] • Highly skewed: limited upside, high downside • Correlation risk: assuming independence will underestimate risks • Concentration risk: sensitivity to the largest loans • Credit VaR= WCLα– E[CL] • Difference between expected losses and certain quantile of losses (worst-case losses) • Expected losses covered by ptf’s earnings • Unexpected losses covered by capital

  7. How to measure credit exposure? • CE = the dollar amount that we lose in case of default with zero recovery • Only the positive economic value counts: CEt= max(Vt, 0) • Vt: credit portfolio’s value • Loans and bonds: direct exposures • Usually, CE measured at par • Derivatives and guarantees: potential CE may be much higher than the current CE • Expected credit exposure • Worst-casecredit exposure (e.g., with 90% confidence level )

  8. How to predict the recovery rate? • Highly variable, neglected by research for a long time • Fragmented and unreliable data • Altman: US, 1971-1995 • On average, RR about 40% with St.Dev. of 20-30%

  9. Which factors influencethe recovery rate? • Collateral or guarantees • Seniority • Industry • Out-of-court restructuring (distressed exchange) vs. • …bankruptcy, which largely depends on the legislation: • Large cross-country differences: e.g., US and France more obligor-friendly than UK • US bankruptcy procedures: ch. 11 (aim to restructure the obligor) vsch. 7 (aim to liquidate the obligor and pay off debt) • Business cycle & average rating in the industry • The obligor’s rating and equity ratio prior to default • Presence of CDS

  10. Recovery rate: beta distribution • Modelling RR: beta distribution with density f(x)=cxa(1-x)b • For mean μ and variance σ2: a=μ2(1-μ)/σ2, b=μ(1-μ)2/σ2

  11. How to measure probability of default? • Internal approach: analyze • Business risk: company position, industry characteristics, country risk • Financial risk: risk tolerance, capital structure, leverage, liquidity, cash flows • Result: credit ratings • External approach: using market data on • Bonds: credit spread • Stocks: equity ratio and return volatility

  12. Credit ratingsby independent agencies • Integral estimate of the company’s solvency based on PD (and possibly RR) • S&P: “forward-looking opinion about creditworthiness of an obligor” • Moody’s: “future ability… of an issuer to make timely payments of principal and interest on a specific fixed-income security” • The issuer pays for the rating • …and later decides whether to disclose it or not • Comprehensive analysis • Quantitative and qualitative (business perspectives, risk attitude,...) • The rating committee decides by voting • The rating manager cannot ensure the outcome • The rating is reconsidered at least once a year

  13. Long-term debt rating • Investment rating: from BBB (S&P), Baa (M’s) • Instruments for conservative investors • …vs. speculative (junk) rating • “Through-the-cycle” approach: credit risk at the worst point in cycle over the contract’s maturity (stressed PD) • …in contrast to the “point-in-time” approach: credit risk depending on the current macro environment (unstressed PD) • Other ratings • Short-term company rating / Rating of specific instruments

  14. What lies beneath the rating? Source: S&P

  15. How to estimate historical PD for each rating? • Cumulative default rate up to year T • Proportion of issuers in default, relative to year 0 • Longer horizon implies fewer observations and lower precision • Sample period should include at least 20 years • Need to accumulate default statistics, esp. for first-class issuers • ...and account for different stages of the business cycles • Estimate for each group of firms that had a given rating in the beginning of the period: Average PD= # defaults / # firms • Choose the type of bonds • Straight bonds vs. convertible/redeemable bonds • Bonds traded in the US market vs. bonds of US companies • Newly issued bonds vs. seasoned bonds

  16. Default rates are inversely related to ratings

  17. What are the drawbacks of credit ratings? • Ratings are too conservative • Ratings react with a lag to changes in the issuer’s solvency • The measurement error is large • Esp. for high ratings and long horizons • Historical PD may underestimate the real risks • Past 20 years could be ‘lucky’ compared to the future • Low competition • S&P, Moody’s control 80% market share • Conflict of interests • Long-term relations with the largest IBs that issued many structured products

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