Recovering Your Money: Insights Into Losses From Defaults
by Karen Van de Castle of Standard & Poor's, and
June 16, 1999
Beginning Paragraphs: Recovery data has always been a weak link of quantitative credit loss models and has long lagged research done on default and migration. Modeling, whether it be to estimate return on capital, return on risk-adjusted capital, value at risk, or pricing, utilizes increasingly sophisticated methods of predicting default based on credit rating equity, price volatility and financial statistics. This default analysis has tended to be paired with static loss assumptions with, at best, a single average used for all secured loans and another single average used for all unsecured loans. Unsurprisingly, the result has been loss given default assumptions marred by high standard deviations and fat tails (excess data points at both ends of the distribution). An unfortunate result of this high standard deviation is an inability to fine-tune spreads, capital allocation and ratings based on historical loss experience.
Published in: Credit Week, Vol. 16. (June 1999), pp. 29-34.