Pricing path-dependent Bermudan options using Wiener chaos expansion: an embarrassingly parallel approach

Jérôme Lelong ()
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Jérôme Lelong: DAO - Données, Apprentissage et Optimisation - LJK - Laboratoire Jean Kuntzmann - Inria - Institut National de Recherche en Informatique et en Automatique - CNRS - Centre National de la Recherche Scientifique - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes

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Abstract: In this work, we propose a new policy iteration algorithm for pricing Bermudan options when the payoff process cannot be written as a function of a lifted Markov process. Our approach is based on a modification of the well-known Longstaff Schwartz algorithm, in which we basically replace the standard least square regression by a Wiener chaos expansion. Not only does it allow us to deal with a non Markovian setting, but it also breaks the bottleneck induced by the least square regression as the coefficients of the chaos expansion are given by scalar products on the L^2 space and can therefore be approximated by independent Monte Carlo computations. This key feature enables us to provide an embarrassingly parallel algorithm.

Keywords: Wiener chaos expansion; high performance computing; regression methods; path-dependent Bermudan options; optimal stopping (search for similar items in EconPapers)
Date: 2020-09
Note: View the original document on HAL open archive server: https://hal.univ-grenoble-alpes.fr/hal-01983115v2
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Citations: View citations in EconPapers (2)

Published in The Journal of Computational Finance, 2020, 24 (2), pp.1-31. ⟨10.21314/JCF.2020.394⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-01983115

DOI: 10.21314/JCF.2020.394

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