This paper addresses the problem of approximating the future value distribution of a large and heterogeneous life insurance portfolio which would play a relevant role, for instance, for solvency capital requirement valuations. Based on a metamodel, we first select a subset of representative policies in the portfolio. Then, by Monte Carlo simulations we obtain a rough estimate of the policies’ value at the chosen future date and finally we approximate the distribution of a single policy and of the entire portfolio by means of two different approaches, the ordinary least squares, and a regression method based on the class of generalized beta of the second kind distributions. Extensive numerical experiments are provided to assess the performance of the proposed models.
