Portfolio Models of Credit Loss II by Edu Pristine

video locked

About the Lecture

The lecture Portfolio Models of Credit Loss II by Edu Pristine is from the course ARCHIV Credit Risk. It contains the following chapters:

  • Credit Metrics contd...
  • Monte Carlo Simulation
  • KMV approach
  • Distance to Default
  • Estimation of Default Correlations

Author of lecture Portfolio Models of Credit Loss II

 Edu Pristine

Edu Pristine


Customer reviews

(1)
5,0 of 5 stars
5 Stars
5
4 Stars
0
3 Stars
0
2 Stars
0
1  Star
0


Excerpts from the accompanying material

... However, instead of a standardized default point, standardized rating migration points are determined for each borrower on basis of its present rating. ...

... simulated rating of each borrower is determined. Any rating upgrade leads to MTM gains in the value of exposure, while rating downgrade reduces the value of exposure. MTM value of all exposures are added to determine portfolio value for each scenario. ...

... probability of default is a function of the firm’s capital structure, the volatility of the asset returns and the current asset value. The EDF is firm specific, and can be mapped onto any rating system to derive the equivalent rating of the obligor. ...

... the value of the equity is determined by the stock market. The information contained in the firm’s stock price and balance sheet can then be translated into an implied risk ...

... default threshold is a linear combination of short-term and long-term liabilities. A practical rule is given ...

... we can use the following formula, a more precise formula is given below ...

... The distance to default is closest to: A. 6.8 standard deviations, B. 5.2 standard deviations, C. 8.1 standard deviations, D. 4.1 standard deviations. Solution: A. ...

... - This credit risk is based on potential rating changes over the year, - A factor to be consider here is portfolio assessment is the correlation between changes in credit ratings and default correlation for any two obligators ...

... Each scenario is characterised by ‘n’ standardised asset returns, one for each of the ‘n’ obligors in the portfolio. 4. For each scenario, and for each obligor, map the standardised asset return into the corresponding rating, according to the threshold levels derived in step 1. 5. Given the spread curves, which apply for each rating, revalue the portfolio. 6. Repeat the procedure ...