On the tail behaviour of aggregated random variables

Jordan Richards and Jonathan A. Tawn

Journal of Multivariate Analysis, 2022, vol. 192, issue C

Abstract: In many areas of interest, modern risk assessment requires estimation of the extremal behaviour of sums of random variables. We derive the first order upper-tail behaviour of the weighted sum of bivariate random variables under weak assumptions on their marginal distributions and their copula. The extremal behaviour of the marginal variables is characterised by the generalised Pareto distribution and their extremal dependence through subclasses of the limiting representations of Ledford and Tawn (1997) and Heffernan and Tawn (2004). We find that the upper-tail behaviour of the aggregate is driven by different factors dependent on the signs of the marginal shape parameters; if they are both negative, the extremal behaviour of the aggregate is determined by both marginal shape parameters and the coefficient of asymptotic independence (Ledford and Tawn, 1996); if they are both positive or have different signs, the upper-tail behaviour of the aggregate is given solely by the largest marginal shape. We also derive the aggregate upper-tail behaviour for some well known copulae which reveals further insight into the tail structure when the copula falls outside the conditions for the subclasses of the limiting dependence representations.

Keywords: Aggregation; Coefficient of asymptotic independence; Conditional extremes; Copula models; Extreme value theory; Generalised pareto distribution (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1016/j.jmva.2022.105065

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