2007/03/18 by Luca Capriotti, Capriotti, Luca
Economics, Econometrics and Finance · Mathematics · Physics and Astronomy · #Computational Finance (q-fin.CP) #Computational Physics (physics.comp-ph) #FOS: Economics and business #FOS: Mathematics #FOS: Physical sciences #Other Condensed Matter (cond-mat.other) #Physics and Society (physics.soc-ph) #Statistics Theory (math.ST) #cond-mat.other #math.ST #physics.comp-ph #physics.soc-ph #q-fin.CP #stat.TH
paper · pdf · doi:10.48550/arxiv.physics/0703181
12 pages, 4 figures
arxiv created 2007/03/18 · arxiv updated 2009/12/01
We describe a simple Importance Sampling strategy for Monte Carlo simulations based on a least squares optimization procedure. With several numerical examples, we show that such Least Squares Importance Sampling (LSIS) provides efficiency gains comparable to the state of the art techniques, when the latter are known to perform well. However, in contrast to traditional approaches, LSIS is not limited to the determination of the optimal mean of a Gaussian sampling distribution. As a result, it outperforms other methods when the ability to adjust higher moments of the sampling distribution, or to deal with non-Gaussian or multi-modal densities, is critical to achieve variance reductions.