Two maxentropic approaches to determine the probability density of compound risk losses

TL;DR

This paper compares two maxentropic methods, SME and MEM, for estimating the probability density of compound risk losses using fractional moments, with applications to risk measures like VaR and TVaR.

Contribution

It introduces and evaluates two maxentropic procedures for density estimation of compound sums, extending previous work and enabling loss disaggregation from total loss data.

Findings

Both methods provide accurate density reconstructions.

The procedures yield reliable VaR and TVaR estimates.

The approaches are robust across different loss distributions.

Abstract

Here we present an application of two maxentropic procedures to determine the probability density distribution of compound sums of random variables, using only a finite number of empirically determined fractional moments. The two methods are the Standard method of Maximum Entropy (SME), and the method of Maximum Entropy in the Mean (MEM). We shall verify that the reconstructions obtained satisfy a variety of statistical quality criteria, and provide good estimations of VaR and TVaR, which are important measures for risk management purposes. We analyze the performance and robustness of these two procedures in several numerical examples, in which the frequency of losses is Poisson and the individual losses are lognormal random variables. As side product of the work, we obtain a rather accurate description of the density of the compound random variable. This is an extension of a previous application based on the Standard Maximum Entropy approach (SME) where the analytic form of the Laplace transform was available to a case in which only observed or simulated data is used. These approaches are also used to develop a procedure to determine the distribution of the individual losses through the knowledge of the total loss. Then, in the case of having only historical total losses, it is possible to decompound or disaggregate the random sums in its frequency/severity distributions, through a probabilistic inverse problem.