Monte Carlo Simulation for Trading Under a L\'evy-Driven Mean-Reverting Framework

TL;DR

This paper introduces a Monte Carlo simulation approach for pairs trading using a Le9vy-driven Ornstein-Uhlenbeck process, allowing for more flexible modeling of mean-reverting spreads with jump processes.

Contribution

It develops a Monte Carlo method with variance reduction for optimal trading in Le9vy-driven mean-reverting models, including bivariate spreads with correlation effects.

Findings

Optimal trading strategies depend on model parameters.

Variance gamma process captures jump behavior in spreads.

Correlation influences bivariate trading strategies.

Abstract

We present a Monte Carlo approach to pairs trading on mean-reverting spreads modeled by L\'evy-driven Ornstein-Uhlenbeck processes. Specifically, we focus on using a variance gamma driving process, an infinite activity pure jump process to allow for more flexible models of the price spread than is available in the classical model. However, this generalization comes at the cost of not having analytic formulas, so we apply Monte Carlo methods to determine optimal trading levels and develop a variance reduction technique using control variates. Within this framework, we numerically examine how the optimal trading strategies are affected by the parameters of the model. In addition, we extend our method to bivariate spreads modeled using a weak variance alpha-gamma driving process, and explore the effect of correlation on these trades.