Tail asymptotics of randomly weighted large risks
Alexandru V. Asimit, Enkelejd Hashorva, Dominik Kortschak
TL;DR
This paper studies the tail behavior of large risks that are asymptotically independent, focusing on linear combinations of weighted order statistics, with applications to Log-Normal risks.
Contribution
It provides new asymptotic approximations for tail probabilities of weighted risk combinations under various assumptions, emphasizing the role of individual tail behavior.
Findings
Derived tail asymptotics for linear combinations of risks
Applied results to Log-Normal risk models
Highlighted the impact of individual tail behavior on aggregate risk
Abstract
In this paper we are concerned with a sample of asymptotically independent risks. Tail asymptotic probabilities for linear combinations of randomly weighted order statistics are approximated under various assumptions, where the individual tail behaviour has a crucial role. An application is provided for Log-Normal risks.
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Taxonomy
TopicsFinancial Risk and Volatility Modeling · Probability and Risk Models · Credit Risk and Financial Regulations