Minimum Variance Importance Sampling via Population Monte Carlo

Variance reduction has always been a central issue in Monte Carlo experiments.Population Monte Carlo can be used to this effect, in that a mixture of importancefunctions, called a D-kernel, can be iteratively optimised to achieve the minimumasymptotic variance for a function of interest among all possible mixtures. Theimplementation of this iterative scheme is illustrated for the computation of theprice of a European option in the Cox-Ingersoll-Ross model,