Least Squares Importance Sampling for Monte Carlo Security Pricing

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dc.creatorCapriotti, Luca
dc.date2007-03-18
dc.date.accessioned2026-07-07T12:11:28Z
dc.date.available2026-07-07T12:11:28Z
dc.descriptionWe 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.
dc.description12 pages, 4 figures
dc.identifierhttps://arxiv.org/abs/physics/0703181
dc.identifierhttp://arxiv.org/abs/physics/0703181
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/210230
dc.subjectPhysics and Society
dc.subjectOther Condensed Matter
dc.subjectStatistics Theory
dc.subjectComputational Physics
dc.subjectComputational Finance
dc.titleLeast Squares Importance Sampling for Monte Carlo Security Pricing
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