Second Order Asymptotics of Aggregated Log-Elliptical Risk

Dominik Kortschak () and Enkelejd Hashorva ()
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Dominik Kortschak: Université de Lyon
Enkelejd Hashorva: University of Lausanne

Methodology and Computing in Applied Probability, 2014, vol. 16, issue 4, 969-985

Abstract: Abstract In this paper we establish the error rate of first order asymptotic approximation for the tail probability of sums of log-elliptical risks. Our approach is motivated by extreme value theory which allows us to impose only some weak asymptotic conditions satisfied in particular by log-normal risks. Given the wide range of applications of the log-normal model in finance and insurance our result is of interest for both rare-event simulations and numerical calculations. We present numerical examples which illustrate that the second order approximation derived in this paper significantly improves over the first order approximation.

Keywords: Risk aggregation; Second order asymptotics; Log-elliptical distribution; Log-normal distribution; Gumbel max-domain of attraction; 60G15 (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11009-013-9356-5

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