An approximation method for risk aggregations and capital allocation rules based on additive risk factor models

Ming Zhou, Jan Dhaene, Jing Yao

Research output: Contribution to journalArticle

3 Citations (Scopus)
6 Downloads (Pure)

Abstract

This paper proposes the use of convex lower bounds as approximation to evaluate the aggregation of risks, based on additive risk factor models in the multivariate generalized Gamma distribution context. We consider two types of additive risk factor model. In Model 1, the risk factors that contribute to the aggregation are deterministic. In Model 2, we consider contingent risk factors. We work out the explicit formulae of the convex lower bounds, by which we propose an analytical approximate capital allocation rule based on the conditional tail expectation. We conduct stress tests to show that our method is robust across various dependence structures.

Original languageEnglish
Pages (from-to)92-100
Number of pages9
JournalInsurance: Mathematics and Economics
Volume79
Early online date10 Jan 2018
DOIs
Publication statusPublished - Mar 2018

Keywords

  • Approximation
  • Capital allocation
  • Convex lower bound
  • Generalized Gamma distribution
  • Risk aggregation

ASJC Scopus subject areas

  • Statistics and Probability
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty

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