DocumentCode :
1802583
Title :
Computing Worst-Case Tail Probabilities Incredit Risk
Author :
Ghosh, Soumyadip ; Juneja, Sandeep
Author_Institution :
IBM Thomas J. Watson Res. Center, Yorktown Heights, NY
fYear :
2006
fDate :
3-6 Dec. 2006
Firstpage :
246
Lastpage :
254
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
Keywords :
investment; chessboard distributions; credit risk; joint distribution; value-at-risk risk measure; worst-case tail probabilities; Banking; Computational modeling; Covariance matrix; Instruments; Pairwise error probability; Performance analysis; Portfolios; Risk analysis; Risk management; Tail;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Simulation Conference, 2006. WSC 06. Proceedings of the Winter
Conference_Location :
Monterey, CA
Print_ISBN :
1-4244-0500-9
Electronic_ISBN :
1-4244-0501-7
Type :
conf
DOI :
10.1109/WSC.2006.323080
Filename :
4117612
Link To Document :
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