Least Squares Importance Sampling for Monte Carlo Security Pricing
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
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Notes
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Postprint
begutachtet (peer reviewed)
In: Quantitative Finance ; 8 (2008) 5 ; 485-497
- Classification
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Wirtschaft
- Event
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Veröffentlichung
- (where)
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Mannheim
- (when)
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2008
- Creator
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Capriotti, Luca
- DOI
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10.1080/14697680701762435
- URN
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urn:nbn:de:0168-ssoar-221168
- Rights
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Open Access unbekannt; Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
25.03.2025, 1:44 PM CET
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Capriotti, Luca
Time of origin
- 2008
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Applications of least-squares regressions to pricing and hedging of financial derivatives
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Least squares in sampling complexity and statistical learning
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Least-squares collocation
Resurrecting weighted least squares
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American option pricing with importance sampling and shifted regressions
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Least-squares prediction of runoff