Journal of Applied Probability

Aggregation of log-linear risks

Paul Embrechts, Enkelejd Hashorva, and Thomas Mikosch

Abstract

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.

Article information

Source
J. Appl. Probab., Volume 51A (2014), 203-212.

Dates
First available in Project Euclid: 2 December 2014

Permanent link to this document
https://projecteuclid.org/euclid.jap/1417528476

Digital Object Identifier
doi:10.1239/jap/1417528476

Mathematical Reviews number (MathSciNet)
MR3317359

Zentralblatt MATH identifier
1334.60029

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

Keywords
Risk aggregation log-linear model subexponential distribution Gumbel max-domain of attraction

Citation

Embrechts, Paul; Hashorva, Enkelejd; Mikosch, Thomas. Aggregation of log-linear risks. J. Appl. Probab. 51A (2014), 203--212. doi:10.1239/jap/1417528476. https://projecteuclid.org/euclid.jap/1417528476


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References

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