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Understanding Stochastic Exposures and LGDs in Portfolio Credit Risk

by Dan Rosen of Algorithmics, and
Marina Sidelnikova of Algorithmics

Spring 2002

Abstract: 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.

Published in: Algo Research Quarterly, Vol. 5, No. 1, (Spring 2002), pp. 43-56.

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