Monte Carlo Methods for Pricing American Options

Raul Chavez Aquino (), Fabian Bastin (), Maria Benazzouz and Mohamed Kharrat ()
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Raul Chavez Aquino: Université de Montréal, Department of Economics
Fabian Bastin: Université de Montréal, and CIRRELT, Department of Computer Science and Operations Research
Maria Benazzouz: Université de Montréal, Department of Economics
Mohamed Kharrat: Jouf University, Department of Mathematics

A chapter in Advances in Modeling and Simulation, 2022, pp 1-20 from Springer

Abstract: Abstract American options are widespread in the financial market. We review various popular techniques used to value American options, as well as Malliavin calculus and recent approaches proposed in machine learning, and examine their performance on synthetic and real data. Our preliminary results confirm that pricing an American put option on a single asset can be efficiently done using regression approaches, and random forests are competitive in terms of accuracy and computation times. Malliavin calculus, despite its interesting mathematical properties, is not competitive for American option pricing, and neural networks are difficult to design in the context of options. Variance reduction, achieved here by means of control variates, is a crucial tool to obtain reliable results at a reasonable cost.

Keywords: American options; Monte Carlo; Dynamic programming; Variance reduction; Control variates (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-10193-9_1

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DOI: 10.1007/978-3-031-10193-9_1

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