Default Risk: Quantitative Methodologies by Edu Pristine

Author of lecture Default Risk: Quantitative Methodologies

Edu Pristine

Customer reviews

Excerpts from the accompanying material

... calculate the value of debt and equity from that information. The value of a firm's debt can serve as an indicator of the firm's default risk. The basic equation is : V = D + E. As E & D are contingent claims, option pricing can be used to determine their values. ...

... such as loans and privately placed bonds. 2 main approaches for quantifying risk: First approach: Consider only Default or No Default (Builds Binomial Tree). Second approach: Risk Adjusted Return on Capital (RAROC) which ...

... Step 3: Calculate credit quality correlations: correlations are inferred from correlations in asset prices. 4 Compute Exposure. Profile of each asset. Compute the volatility of value caused by up(down) grades ...

... Loans face value. Loan commitments face value. Receivables face amount  ...

... Each transition will result in change in value (derived from credit spread date and in default, recovery rates). Each value is weighted by its likelihood to create a distribution across each credit state. The senior unsecured credit rating of the issuer indicates the probability of default or transition to any other possible credit quality ...

... revaluation upon upgrades or downgrades ...

... volatility of value due to credit quality changes. Calculating volatility in value due to credit quality changes for a single exposure 9 Year-end Rating Probability of state (%) New bond Value plus Coupon ($) Probability weighted Value ...

... a distribution of values for a portfolio of many bonds. A random sample of ...

... 1 Correlation of Borrower behavior 2 Credit risk Specific risk: driven by ...

... Actual Distribution 5% VaR = 95% of actual distribution = $107.09 - $102.02 = $5.07. ...

... CreditMetrics solves for correlations by first regressing equity returns on industry indices. The correlation between any pair of equity returns is calculated using the correlations across the industry indices. Once we obtain equity correlations, we can solve for ...

... The value of the firm is observable and follows a lognormal diffusion process. The risk-free interest rate is constant throughout time ...

... techniques to assess credit risk. Bondholders are paid first, equity holders have a residual claim. If market value of assets ...

... Level of debt obligations - Volatility of firm's value - EDF is calculated on a day-to-day basis. Supposed to be forward-looking and able to outperform ...

... 1 Yr Distribution of asset value at horizon ...

... use the following formula. A more precise formula is given below. E(RoA): expected return on assets V: Value of the firm assets ...

... theoretical EDF (what is the distribution of asset return outcomes) 2. Problems in applying model to private companies and thinly-traded companies 3. Results sensitive to stock market movements ...

... events in a time period such as a year (within a portfolio of obligors having a range of different annual probabilities of default). Annual probability of default ...

... Default probability is continuous determined, i. e., implied - Input: Stock-prices ('equity call-option total assets), default risk, arbitrage-model, arbitrage CreditRisk: ...

... but also less realistic. Market-model (given time-horizon) usefull for solvency calculations. KMV: Based on market volatility. Using stock-prices makes model more ...

... of independent variables. Non-triviality: It should produce appealing outcomes. Feasibility: The model should use accessible resources in a reasonable amount ...

... An example of linear discriminant analysis is Altman-Z score Parametric Discrimination. This uses a score to determine the members of the sub-group. Examples include logit and probit models. Parametric discriminant analysis determines if a score falls above or below a certain threshold. This determines which subgroup the observation will be ...

... to either minimize the probability of misclassification or minimize the loss associated with that error Neyman-Pearson. This uses the statistical concept of Type I and Type II errors. Type I occurs when a bank lends to a risky firm because it was incorrectly accepted as a non-risky firm. Type II occurs when a bank refuses to lend to a non-risky firm because ...

... unity for both the axis. Ideally the ray should have infinite slope indicating that all defaults were correctly predicted. If the ray has a 45 degree slope then there are equal proportions of both mistakes. The cumulative accuracy profile (CAP) compares the probability of default computed by the ...

... consider when choosing between models is as follows: Ease of use Robustness of the model when new data ...