Pricing American option under exponential Levy Jump-diffusion model using Random Forest instead of least square regression

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Mohamed Maidoumi; Mehdi Zahid; Boubker Daafi

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Journal Paper

Paper Information

Journal: Journal of Mathematical Modeling Year:2023 | Volume:11 | Issue:2 Page(s): 229-244

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Title

Pricing American option under exponential Levy Jump-diffusion model using Random Forest instead of least square regression

Author(s)

Mohamed Maidoumi | Mehdi Zahid | Boubker Daafi | Issue Writer Certificate

Keywords

Monte Carlo simulation

Levy jump-diffusion model

Longstaff and Schwartz algorithm

American option

Random Forest RI regression

Microsoft ``MSFT" put option

Dynamic programming

Abstract

In this paper, we aim to propose a new hybrid version of the Longstaff and Schwartz algorithm under the exponential Levy jump-diffusion model using Random Forest regression. For this purpose, we will build the evolution of the option price according to the number of paths. Further, we will show how this approach numerically depicts the convergence of the option price towards an equilibrium price when the number of simulated trajectories tends to a large number. In the second stage, we will compare this hybrid model with the classical model of the Longstaff and Schwartz algorithm (as a benchmark widely used by practitioners in pricing American options) in terms of computation time, numerical stability and accuracy. At the end of this paper, we will test both approaches on the Microsoft share “MSFT” as an example of a real market.

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