A general importance sampling algorithm for estimating portfolio loss probabilities in linear factor models

This paper develops a novel importance sampling algorithm for estimating the probability of large portfolio losses in the conditional independence framework. We apply exponential tilts to (i) the distribution of the natural sufficient statistics of the systematic risk factors and (ii) conditional de...

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Bibliographic Details
Published in:	Insurance, mathematics & economics Vol. 64; pp. 279 - 293
Main Authors:	Scott, Alexandre, Metzler, Adam
Format:	Journal Article
Language:	English
Published:	Amsterdam Elsevier B.V 01.09.2015 Elsevier Sequoia S.A
Subjects:	[formula omitted] copula Algorithms Copulas Cross-entropy method Exponential tilts Gaussian copula Importance sampling Kullback–Leibler divergence Mathematical models Monte Carlo Parameter estimation Portfolio loss Portfolio performance Portfolios Probability Risk factors Sampling Statistics Studies Values Kullback–Leibler divergence t copula Monte Carlo Cross-entropy method Portfolio loss Gaussian copula Importance sampling Exponential tilts
ISSN:	0167-6687, 1873-5959
Online Access:	Get full text
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