A Maximum Entropy Approach to the Loss Data Aggregation Problem

One of the main problems in the advanced measurement approach to determine operational risk regulatory capital, consists of the computation of the distribution of losses when the data is made up of aggregated losses caused by different types of risk events in different business lines. A similar problem appears in the insurance industries when there is need of aggregating losses of different types. When the data is well collected, that is, when the losses are collected as a joint vector, the maxentropic techniques are quite suitable for finding the probability density of the aggregated loss. When the data is not well collected, the maxentropic procedure provides us with the marginal densities, which can then be coupled by means of some appropriate copula, and then carry on one of the two procedures that we apply. At any rate, the two possibilities hinge in an essential way on the maxentropic technique to determine a probability density from its Laplace transform. This is due to the fact that such techniques provide us with analytical expressions for the densities, which makes many numerical procedures easier to implement. It is the aim of this note, to examine and compare these alternative ways of solving the problem of determining the density of aggregate losses