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

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.

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

Monte Carlo simulation , Levy jump-diffusion model , Longstaff and Schwartz algorithm , American option , Random Forest RI regression , Microsoft ``MSFT" put option , Dynamic programming