An improved least squares Monte Carlo valuation method based on heteroscedasticity

Convergence of the Least Squares Monte Carlo Approach to American Option Valuation

In a recent paper Longstaff & Schwartz (2001) suggest a method to American option valuation based on simulation. The method is termed the Least Squares Monte-Carlo (LSM) method, and although it has become widely used not much is known about the properties of the estimator. This paper corrects this shortcoming using theory from the literature on seminonparametric series estimators. A central par...

rlsm: R package for least squares Monte Carlo

This short paper briefly describes the implementation of the least squares Monte Carlo method in the rlsm package. This package provides users with an easy manner to experiment with the large amount of R regression tools on any regression basis and reward functions. This package also computes lower and upper bounds for the true value function via duality methods.

Least Squares Monte Carlo Approach to Convex Control Problems

Optimal control problems with convex functions are ubiquitous in applications of stochastic optimization. However, when applied in this context, the classical least squares Monte Carlo methodology makes no attempt to take advantage of this special structure: Given the convexity of value functions, it seems reasonable to search for the best least-squares fit among the elements of a cone of conve...

Pricing Bounds for VIX Derivatives via Least Squares Monte Carlo

Derivatives on the Chicago Board Options Exchange volatility index (VIX) have gained significant popularity over the last decade. The pricing of VIX derivatives involves evaluating the square root of the expected realised variance which cannot be computed by direct Monte Carlo methods. Least squares Monte Carlo methods can be used but the sign of the error is difficult to determine. In this pap...

Controlling for Common Method Variance with Partial Least Squares Path modeling: A Monte Carlo Study