Statistical arbitrage is a classic trading strategy, invented in the 1980s. We mostly see it being applied in the equity markets, but statistical arbitrage is not limited to equities. It can be applied to other asset classes as well. Reference [1] examined the statistical arbitrage strategy in the commodity markets, specifically crude oil. The author pointed out,
In this paper, we introduce the concept of statistical arbitrage through the definition of a trading strategy, called mispricing portfolio. We focus on mean-reverting strategies in order to capture persistent anomalies in the markets. Furthermore, we show how we identify statistical arbitrages and apply trading rules adopted from equity markets.
We show the empirical evidence of statistical arbitrage in crude oil markets. We have built the mispricing portfolio by using a cointegration regression in order to identify long-term pricing relationships between the WTI crude oil futures and the price of a replication portfolio composed of other two crude oils, Brent and Dubai. Finally, we apply trading rules commonly used in equity markets to profit.
Basically, the author utilized cointegration to construct a statistical arbitrage portfolio. Various methods were then used to test for stationarity and mean reversion: the Quandt likelihood ratio (QLR), augmented Dickey-Fuller (ADF) test, autocorrelations, and the variance ratio. The constructed strategy performed well both in- and out-of-sample.
This is another interesting aspect of statistical arbitrage, applied in a different market. We note, however, that the period tested was up to 2017, i.e., pre-COVID.
It would be interesting to see the results during COVID, to observe how the strategy withstood the period when the front-month oil futures price went negative.
Let us know what you think in the comments below or in the discussion forum.
References
[1] Viviana Fanelli, Mean-Reverting Statistical Arbitrage Strategies in Crude Oil Markets, Risks 2024, 12, 106.
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