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Aggregation of log-linear risks

Part of: Stochastic processes

Published online by Cambridge University Press:  30 March 2016

Paul Embrechts ,
Enkelejd Hashorva  and
Thomas Mikosch
Paul Embrechts
Affiliation:
ETH Zürich and Swiss Finance Institute, Department of Mathematics, ETH Zürich, 8092 Zürich, Switzerland. Email address: paul.embrechts@math.ethz.ch.
Enkelejd Hashorva
Affiliation:
Faculty of Business and Economics (HEC Lausanne), University of Lausanne, 1015 Lausanne, Switzerland
Thomas Mikosch
Affiliation:
Department of Mathematics, University of Copenhagen, Universitetsparken 5, DK-2100 Copenhagen, Denmark.
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Abstract

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In this paper we work in the framework of a k-dimensional vector of log-linear risks. Under weak conditions on the marginal tails and the dependence structure of a vector of positive risks, we derive the asymptotic tail behaviour of the aggregated risk and present an application concerning log-normal risks with stochastic volatility.

Keywords

Risk aggregationlog-linear modelsubexponential distributionGumbel max-domain of attraction

MSC classification

Primary: 60G15: Gaussian processes
Secondary: 60G70: Extreme value theory; extremal processes

Information

Type
Part 5. Finance and econometrics
Information
Journal of Applied Probability , Volume 51 , Issue A: Celebrating 50 Years of The Applied Probability Trust , December 2014 , pp. 203 - 212
Copyright
Copyright © Applied Probability Trust 2014 

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