The Acceleration of Least Squares Monte Carlo in Risk Management

The Least Squares Monte Carlo (LSMC) method was first proposed by Longstaff and Schwartz [1] to price the American option, since then it has been applied in different industries from banking [2] to energy sector [3]. In the last decade, there is an increasing demand for sophisticated risk modeling [4]. To overcome the computational complexity of those models, the proxy techniques have gain popularity in both risk management practice and research over the last decade [5]. The idea of proxy is to approximate the original model with less features to reduce the computational complexity while keeping sufficient accuracy. Among the various proxy techniques, LSMC is a state-of-the-art approach. However, the polynomial of LSMC is still too complicated in multidimensional problems. There are several works that discussed how to further improve the computational speed of LSMC. AS.Chen and PF Shen [6] studied the computational complexity of LSMC. A.R. Choudhury [7] parallelized the LSMC algorithm for American option pricing. Another method to speed up LSMC is focusing on Monte Carlo simulation itself, using techniques such as Quasi- Monte Carlo to make LSMC more efficient [8].