Gaussian Approximation of Perturbed Chi-Square Risks
Krzysztof Debicki, Enkelejd Hashorva, Lanpeng Ji
TL;DR
This paper demonstrates that the conditional distribution of perturbed chi-square risks can be effectively approximated by Gaussian and other distributions, with applications in extreme value theory and multivariate extremes.
Contribution
It introduces a novel approximation method for perturbed chi-square risks' conditional distributions, enhancing modeling in extreme value analysis.
Findings
Conditional distributions can be approximated by Gaussian distributions.
Applications demonstrated in extreme value models.
Improves understanding of multivariate extreme risks.
Abstract
In this paper we show that the conditional distribution of perturbed chi-quare risks can be approximated by certain distributions including the Gaussian ones. Our results are of interest for conditional extreme value models and multivariate extremes as shown in three applications.
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Taxonomy
TopicsFinancial Risk and Volatility Modeling · Stochastic processes and financial applications · Probability and Risk Models