A singular stochastic control approach for optimal pairs trading with proportional transaction costs

TL;DR

This paper develops a singular stochastic control framework for optimal pairs trading with proportional transaction costs, deriving a nonlinear quasi-variational inequality and providing numerical and empirical validation.

Contribution

It introduces a novel control approach that jointly optimizes trade timing and size in pairs trading with transaction costs, including a convergence-guaranteed numerical scheme.

Findings

The value function solves a nonlinear quasi-variational inequality.

Numerical algorithms effectively compute optimal transaction regions.

Empirical results show strategy efficiency across six U.S. stock pairs.

Abstract

Optimal trading strategies for pairs trading have been studied by models that try to find either optimal shares of stocks by assuming no transaction costs or optimal timing of trading fixed numbers of shares of stocks with transaction costs. To find optimal strategies which determine optimally both trade times and number of shares in pairs trading process, we use a singular stochastic control approach to study an optimal pairs trading problem with proportional transaction costs. Assuming a cointegrated relationship for a pair of stock log-prices, we consider a portfolio optimization problem which involves dynamic trading strategies with proportional transaction costs. We show that the value function of the control problem is the unique viscosity solution of a nonlinear quasi-variational inequality, which is equivalent to a free boundary problem for the singular stochastic control value function. We then develop a discrete time dynamic programming algorithm to compute the transaction regions, and show the convergence of the discretization scheme. We illustrate our approach with numerical examples and discuss the impact of different parameters on transaction regions. We study the out-of-sample performance in an empirical study that consists of six pairs of U.S. stocks selected from different industry sectors, and demonstrate the efficiency of the optimal strategy.