COMMERCIAL AND INDUSTRIAL MATHEMATICS

December 18, 2014

Seminar Series on Quantitative Finance - October 30, 2002

Abstracts

Dan Rosen, VP Product Marketing, Algorithmics
Understanding Stochastic Exposures and LGDs in Portfolio Credit Risk, and their impact in BIS II requirements.
This paper presents a case study on the impact of stochastic exposures and losses given default (LGD) on portfolio credit-risk and its impact on BIS II regulatory requirements. In this sense, four factors have a substantial effect on credit losses: exposure (market) volatility, credit correlations, market-credit correlations, and portfolio granularity. We emphasize the importance of treating stochastic exposures for economic and regulatory capital properly. In particular, we discuss the limitations of the regulatory proposals when market correlations affect exposures/LGDs and when market and credit risk are correlated. Correlated exposures/LGDs and market-credit correlations occur quite frequently and are of sizeable proportions; the latter are the cause of wrong-way exposures. Although the examples in this paper use portfolios of derivatives, the techniques and results apply equally to other cases where LGDs, exposures and spreads are stochastic. This paper presents a case study on the impact of stochastic exposures and losses given default (LGD) on portfolio credit-risk estimation. In this sense, four factors have a substantial effect on credit losses: exposure (market) volatility, credit correlations, market-credit correlations, and portfolio granularity. We emphasize the importance of treating stochastic exposures for economic and regulatory capital properly. In particular, we discuss the limitations of the regulatory proposals when market correlations affect exposures/LGDs and when market and credit risk are correlated. Correlated exposures/LGDs and market-credit correlations occur quite frequently and are of sizeable proportions; the latter are the cause of wrong-way exposures. Although the examples in this paper use portfolios of derivatives, the techniques and results apply equally to other cases where LGDs, exposures and spreads are stochastic.

Greg M. Gupton, Vice President and Senior Analyst at Moody's Risk Management Services
Measures of Debt Security Loss Given Default
In the field of credit risk measurement/management there has been tremendous effort and progress on the specific problems of forecasting obligor credit default and estimating the distribution of possible future portfolio value inclusive of credit defaults. In sharp contrast to this progress there has been relatively little work devoted to forecasting Loss Given Default (LGD) which is the compliment of the "recovery rate" that a security holder will be left with after a credit default. Yet LGD forecasting is proportionally just as important as a Default Probability Forecast. Using a data-set of about 2,000 recovery observations, Moody's|KMV has completed research to make LGD forecasts. We will discuss this and continuing research including: the correlation between LGD and default rate intensities, the cyclically of LGD, and model validation.

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