About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
WSC 2006
Conference paper
Computing worst-case tail probabilities in credit risk
Abstract
Simulation is widely used to measure credit risk in portfolios of loans, bonds, and other instruments subject to possible default. This analysis requires performing the difficult modeling task of capturing the dependence between obligors adequately. Current methods assume a form for the joint distribution of the obligors and match its parameters to given dependence specifications, usually correlations. The value-at-risk risk measure (a function of its tail quantiles) is then evaluated. This procedure is naturally limited by the form assumed, and might not approximate well the "worst-case" possible over all joint distributions that match the given specification. We propose a procedure that approximates the joint distribution with chessboard distributions, and provides a sequence of improving estimates that asymptotically approach this "worst-case" value-at-risk. We use it to experimentally compare the quality of the estimates provided by the earlier procedures. © 2006 IEEE.