Bank6003 Notes
BANK6003 Final Exam Notes
TOPIC 4A: Credit Risk – Estimating Default Probabilities
Overview * Theory of credit risk less developed than VaR based models of market risk. * Much less amenable to precise measurement than market risk – default probabilities are much more difficult to measure than dispersion of market movements. * Measurement on individual loans is important to FI for pricing and setting limits on credit risk exposure.
Default Risk Models
1. Qualitative Models * Assembling relevant information from private and external sources to make a judgement on the probability of default. * Borrower specific factors (idiosyncratic or specific to individual borrower) include: reputation, leverage, volatility of earnings, covenants and collateral. * Market-specific factors (systematic factors that impact all borrowers include): business cycle and interest rate levels. * FI manager weighs these factors to come to an overall credit decision. * Subjective
2. Credit Scoring Models * Quantitative models that use data on observed borrower characteristics to calculate a score that represents borrower’s probability of default or sort borrowers into different default risk categories.
Linear Probability Models (LPMs) * Econometric model to explain repayment experience on past/old loans. * Regression model with a “dummy” dependent variable Z; Z = 1 default and Z=0 no default. * Weakness: no guarantee that the estimated default probabilities will always lie between 0 and 1 (theoretical flaw)
Logit and Probit Models * Developed to overcome weakness of LPM. * Explicitly restrict the estimated range of default probabilities to lie between 0 and 1. * Logit: assumes probability of default to be logistically distributed. * Probit: assumes probability of default has a cumulative normal distribution function.
Linear Discriminant Analysis * Derived from statistical technique called
TOPIC 4A: Credit Risk – Estimating Default Probabilities
Overview * Theory of credit risk less developed than VaR based models of market risk. * Much less amenable to precise measurement than market risk – default probabilities are much more difficult to measure than dispersion of market movements. * Measurement on individual loans is important to FI for pricing and setting limits on credit risk exposure.
Default Risk Models
1. Qualitative Models * Assembling relevant information from private and external sources to make a judgement on the probability of default. * Borrower specific factors (idiosyncratic or specific to individual borrower) include: reputation, leverage, volatility of earnings, covenants and collateral. * Market-specific factors (systematic factors that impact all borrowers include): business cycle and interest rate levels. * FI manager weighs these factors to come to an overall credit decision. * Subjective
2. Credit Scoring Models * Quantitative models that use data on observed borrower characteristics to calculate a score that represents borrower’s probability of default or sort borrowers into different default risk categories.
Linear Probability Models (LPMs) * Econometric model to explain repayment experience on past/old loans. * Regression model with a “dummy” dependent variable Z; Z = 1 default and Z=0 no default. * Weakness: no guarantee that the estimated default probabilities will always lie between 0 and 1 (theoretical flaw)
Logit and Probit Models * Developed to overcome weakness of LPM. * Explicitly restrict the estimated range of default probabilities to lie between 0 and 1. * Logit: assumes probability of default to be logistically distributed. * Probit: assumes probability of default has a cumulative normal distribution function.
Linear Discriminant Analysis * Derived from statistical technique called