A GENERALIZED LOSS RATIO METHOD DEALING WITH UNCERTAIN VOLUME MEASURES

Ulrich Riegel

ASTIN Bulletin, 2018, vol. 48, issue 2, 699-747

Abstract: Unlike chain ladder, the loss ratio method requires volume measures. Typically, these volumes are assumed to be known. In practice, however, accurate volume measures are rarely available. We interpret the available volumes as estimators for the true volume measures and analyze the consequences for the loss ratio method. In particular, we calculate the mean squared error of prediction, including uncertainty of volume measures, and derive approximately optimal weights for the observed incremental loss ratios. We then introduce a generalization of the loss ratio method that is tailored to the situation of uncertain volume measures and calculate the prediction uncertainty of this generalized loss ratio method.

Date: 2018
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