A hidden Markov model for statistical arbitrage in international crude oil futures markets

Abstract

In this work, we study statistical arbitrage strategies in international crude oil futures markets. We analyse strategies that extend classical pairs trading strategies, considering the two benchmark crude oil futures (Brent and WTI) together with the newly introduced Shanghai crude oil futures. We document that the time series of these three futures prices are cointegrated and we model the resulting cointegration spread by a mean-reverting regime-switching process modulated by a hidden Markov chain. By relying on our stochastic model and applying online filter-based parameter estimators, we implement and test a number of statistical arbitrage strategies. Our analysis reveals that statistical arbitrage strategies involving the Shanghai crude oil futures are profitable even under conservative levels of transaction costs and over different time periods. On the contrary, statistical arbitrage strategies involving the three traditional crude oil futures (Brent, WTI, Dubai) do not yield profitable investment opportunities. Our findings suggest that the Shanghai futures, which has already become the benchmark for the Chinese domestic crude oil market, can be a valuable asset for international investors.

Figures (10)

• Figure 1: Weekly futures prices of the three contracts. The black dashed line corresponds to 07/01/2022, which separates the training sample from the test sample.
• Figure 2: Resulting spread process within the test sample.
• Figure 3: ACF and PACF of the spread process over the training sample, weekly observations.
• Figure 4: Recursively updated estimates of $\gamma_i$, $\alpha_i$, $\eta_i$ and $\pi_{ii}$, $i=1,2$, for the $N=2$ AR-HMM.
• Figure 5: One-step ahead forecasts and actual values of $S$ across the test sample.

Theorems & Definitions (7)

• Remark 2.1
• Remark 2.2: Numerical aspects
• Remark 3.1
• Remark 3.2: Alternative statistical arbitrage strategies
• Remark 4.1: Robustness of the estimates to filtering initialization
• Remark 4.2
• Remark 4.3: Robustness of trading strategy performance to filtering initialization