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Least Squares Importance Sampling for Monte Carlo Security Pricing
[journal article]
Abstract 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, for prob... view more
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, for problems that can be formulated in terms of the determination of the optimal mean of a multivariate Gaussian distribution. In addition, LSIS can be naturally applied to more general importance sampling densities and is particularly effective 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.... view less
Classification
Economic Statistics, Econometrics, Business Informatics
Basic Research, General Concepts and History of Economics
Method
theory application
Free Keywords
Monte Carlo methods; Derivatives pricing; Financial derivatives; Financial engineering
Document language
English
Publication Year
2008
Page/Pages
p. 485-497
Journal
Quantitative Finance, 8 (2008) 5
DOI
https://doi.org/10.1080/14697680701762435
Status
Postprint; peer reviewed
Licence
PEER Licence Agreement (applicable only to documents from PEER project)