Sequential Monte Carlo Methods for Option Pricing

Pierre Del Moral 1 Ajay Jasra
1 ALEA - Advanced Learning Evolutionary Algorithms
Inria Bordeaux - Sud-Ouest, UB - Université de Bordeaux, CNRS - Centre National de la Recherche Scientifique : UMR5251
Abstract : In the following paper we provide a review and development of sequential Monte Carlo (SMC) methods for option pricing. SMC are a class of Monte Carlo-based algorithms, that are designed to approximate expectations w.r.t a sequence of related probability measures. These approaches have been used, successfully, for a wide class of applications in engineering, statistics, physics and operations research. SMC methods are highly suited to many option pricing problems and sensitivity/Greek calculations due to the nature of the sequential simulation. However, it is seldom the case that such ideas are explicitly used in the option pricing literature. This article provides an up-to date review of SMC methods, which are appropriate for option pricing. In addition, it is illustrated how a number of existing approaches for option pricing can be enhanced via SMC. Specifically, when pricing the arithmetic Asian option w.r.t a complex stochastic volatility model, it is shown that SMC methods provide additional strategies to improve estimation.
Type de document :
Article dans une revue
Stochastic Analysis and Applications, Taylor & Francis: STM, Behavioural Science and Public Health Titles, 2011, 29 (2), pp.292-316. 〈10.1080/07362994.2011.548993〉
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https://hal.inria.fr/inria-00533433
Contributeur : Pierre Del Moral <>
Soumis le : samedi 6 novembre 2010 - 10:34:40
Dernière modification le : jeudi 11 janvier 2018 - 06:22:36

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Pierre Del Moral, Ajay Jasra. Sequential Monte Carlo Methods for Option Pricing. Stochastic Analysis and Applications, Taylor & Francis: STM, Behavioural Science and Public Health Titles, 2011, 29 (2), pp.292-316. 〈10.1080/07362994.2011.548993〉. 〈inria-00533433〉

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