Prediction Error of the Multivariate Chain Ladder Reserving Method

In the present paper we consider the claims reserving problem in a multivariate context, that is, we study the multivariate Chain-ladder (CL) method for a portfolio of N correlated run-off triangles based on multivariate age-to-age factors. This method allows for a simultaneous study of individual run-off subportfolios and facilitates the derivation of an estimator for the mean square error of prediction (MSEP) for the CL predictor of the ultimate claim of the total portfolio. However, unlike the already existing approaches we replace the univariate CL predictors with multivariate ones. These multivariate CL predictors reflect the correlation structure between the subportfolios and are optimal in terms of a classical optimality criterion, which leads to an improvement of the estimator for the MSEP. Moreover, all formulas are easy to implement on a spreadsheet since they are in matrix notation. We illustrate the results by means of an example.