Mean-Reverting Statistical Arbitrage in Crude Oil Markets

Abstract

In this paper, we introduce the concept of statistical arbitrage through the definition of a trading strategy that captures persistent anomalies in long-run relationships among assets. We devise a methodology to identify and test mean-reverting statistical arbitrage, and to develop trading strategies. We empirically investigate the existence of statistical arbitrage opportunities in crude oil markets. In particular, we focus on long-term pricing relationships between the West Texas Intermediate crude oil futures and a so-called statistical portfolio, composed by other two crude oils, Brent and Dubai. Firstly, the cointegration regression is used to track the persistent pricing equilibrium, and mispricings arise when West Texas Intermediate crude oil price diverges from the statistical portfolio value. Secondly, we verify that mispricing dynamics revert back to equilibrium with a predictable behaviour, and we exploit this stylized fact by applying the trading rules commonly used in equity markets to the crude oil market. The trading performance is measured by three specific profit indicators on out-of-sample data. Lastly, we use a Monte Carlo simulation approach to develop a model for forecasting the expected Value at Risk of the adopted trading strategy over an established holding period.

Keywords: Statistical arbitrage, trading strategy, Value at Risk, commodity markets.