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Least Squares Importance Sampling for Monte Carlo Security Pricing

[Zeitschriftenartikel]

Capriotti, Luca

Zitationshinweis

Bitte beziehen Sie sich beim Zitieren dieses Dokumentes immer auf folgenden Persistent Identifier (PID):http://nbn-resolving.de/urn:nbn:de:0168-ssoar-221168

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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)
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