A numerical approach to the risk capital allocation problem

Henryk Gzyl and Silvia Mayoral

Journal of Risk

Abstract: Capital risk allocation is an important problem in corporate, financial and insurance risk management. There are two theoretical aspects to this problem. The first aspect consists of choosing a risk measure, and the second consists of choosing a capital allocation rule subordinated to the risk measure. When these two complementary aspects are settled, the problem is reduced to a computational problem. No universal prescription for choosing a risk measure exists, let alone a subordinated capital allocation rule. However, expert opinion may be expressed as a range of capital to be allocated to each risk. For example, the managers of each line of activity of a corporation or bank may have a way to estimate a minimum and maximum losses with high confidence. In that case, one is confronted with two interesting numerical problems. First, to devise a method to allocate risk capital within the bounds provided by the expert. Second, to use the upper bounds as stand-alone risk prices to determine a risk measure, which can then be used to compute the prices of other risks. The risk measure could also be used to compute a range for the capital allocations of a new collection of risks, to which our numerical procedure to allocate risk capital can be applied. The aim of this paper is to use a model-free, nonparametric approach based on the method of maximum entropy in the mean to solve both problems and to examine how they are intertwined. In this way we circumvent the problem of choosing between risk valuation and risk pricing methods, and between risk capital allocation rules consistent with them. Also, the maximum-entropy-based methodology provides us with a method to determine coherent risk measures consistent with the bounds provided by expert opinion or management. The risk measure provides us with a method to determine either risk prices or bounds to be used for capital allocation for a different collection of risks.

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