Minimum variance importance samplingviaPopulation Monte Carlo
Open Access
- 17 August 2007
- journal article
- research article
- Published by EDP Sciences in ESAIM: Probability and Statistics
- Vol. 11, 427-447
- https://doi.org/10.1051/ps:2007028
Abstract
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 importance functions, called a D-kernel, can be iteratively optimized to achieve the minimum asymptotic variance for a function of interest among all possible mixtures. The implementation of this iterative scheme is illustrated for the computation of the price of a European option in the Cox-Ingersoll-Ross model. A Central Limit theorem as well as moderate deviations are established for the D-kernel Population Monte Carlo methodology.Keywords
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