Academic Editor: Youssef EL FOUTAYENI
Received |
Accepted |
Published |
10 January 2020 |
25 January 2020 |
10 March 2020 |
Abstract: The Option Pricing Problem began with the works of Black and Scholes appeared in 1973. However, with the rise of robust model for and resolving optimization problems became less time consuming with the currently used computational methods for the Multi Dimensional Backward Stochastic Differential Equation which is resolving problems involving final condition. So in this project we aim explore two Monte Carlo algorithms for pricing multi dimensional American options. First method based on computation of the optimal exercise boundary [1] while the second is about compare between continuation and exercise values based on conditional expectation approximation by least-square regression [2], We also show the efficiency of the two approaches, and comparing this methods with Data scientist approaches especially random forest regression[4].
