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Least Squares Importance Sampling for Monte Carlo Security Pricing
[Zeitschriftenartikel]
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... mehr
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.... weniger
Klassifikation
Wirtschaftsstatistik, Ökonometrie, Wirtschaftsinformatik
Allgemeines, spezielle Theorien und "Schulen", Methoden, Entwicklung und Geschichte der Wirtschaftswissenschaften
Methode
Theorieanwendung
Freie Schlagwörter
Monte Carlo methods; Derivatives pricing; Financial derivatives; Financial engineering
Sprache Dokument
Englisch
Publikationsjahr
2008
Seitenangabe
S. 485-497
Zeitschriftentitel
Quantitative Finance, 8 (2008) 5
DOI
https://doi.org/10.1080/14697680701762435
Status
Postprint; begutachtet (peer reviewed)
Lizenz
PEER Licence Agreement (applicable only to documents from PEER project)