Least Squares Importance Sampling for Monte Carlo Security Pricing

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[Zeitschriftenartikel]

Capriotti, Luca

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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 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.
Klassifikation	Allgemeines, spezielle Theorien und "Schulen", Methoden, Entwicklung und Geschichte der Wirtschaftswissenschaften; Wirtschaftsstatistik, Ökonometrie, Wirtschaftsinformatik
Methode	Theorieanwendung
Freie Schlagwörter	Monte Carlo methods; Derivatives pricing; Financial derivatives; Financial engineering
Publikationsjahr	2008
Seitenangabe	S. 485-497
Zeitschriftentitel	Quantitative Finance, 8 (2008) 5
DOI	http://dx.doi.org/10.1080/14697680701762435
Status	Postprint; begutachtet (peer reviewed)
Lizenz	PEER Licence Agreement (applicable only to documents from PEER project)