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Least Squares Importance Sampling for Monte Carlo Security Pricing

[journal article]

Capriotti, Luca

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)


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© 2007 - 2025 Social Science Open Access Repository (SSOAR).
Based on DSpace, Copyright (c) 2002-2022, DuraSpace. All rights reserved.