Pricing basket options using Monte Carlo simulation employing Cholesky decomposition and variance reduction techniques under the 2D stochastic Black–Scholes equation

This paper examines and applies the Monte Carlo approach to the two-dimensional Black–Scholes partial differential equation, including the Cholesky decomposition to generate correlated Brownian motions to evaluate options on two underlying assets. This study focuses on assessing the performance and risk management of investment portfolios that include these two assets, which are part of the NASDAQ 100 stock index and two active stocks of the S&P500 stock index, namely NVIDIA Corp, Tesla Inc, Apple Inc, and Microsoft Corp over one year from November 30, 2023, to November 30, 2024. The two-dimensional Black–Scholes model is chosen for its ability to capture complex market dynamics involving correlated assets. To optimize the valuation of the basket option (Call - Put), variance minimization techniques, namely control variate and stratified sampling methods, were used. The results highlight how these techniques accurately filter Brownian paths and clarify the impact of as set correlations on market behavior.