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The WTI-Brent spread is the difference between the prices of two types of crude oil, West Texas Intermediate (WTI) on the long side and Brent Crude (Brent) on the short side. The two oils differ only in the ability of WTI to produce slightly more gasoline in the cracking ratio, which causes WTI’s slight pricing margin over Brent.
As both oils are very similar, their spread shows signs of strong predictability and usually oscillates around some average value. It is, therefore, possible to use deviations from the fair spread value to bet on convergence back to fair value. The fair spread value could be calculated via moving average, regression, neural network regression, or other procedures. We present moving average calculation as an example trading strategy from the source paper.
Fundamental reason
Both oils differ in chemical compositions, and they also differ in production and transportation attributes. These differences are reflected in the price spread between both futures contracts. The spread is mean reverting because most of the price shocks are only temporal, so the spread moves back to its long term economic equilibrium, and therefore it is possible to create a trading strategy based on this mean reversion. Caution should be only needed in utilizing parameters from the source paper as they are based on the short history and, therefore, could be susceptible to data-mining bias.
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Market Factors
Confidence in Anomaly's Validity
Notes to Confidence in Anomaly's Validity
Period of Rebalancing
Number of Traded Instruments
Complexity Evaluation
Financial instruments
Backtest period from source paper
Indicative Performance
Notes to Indicative Performance
Estimated Volatility
Notes to Estimated Volatility
Maximum Drawdown
Notes to Maximum drawdown
Sharpe Ratio
Regions
Simple trading strategy
A 20-day moving average of WTI/Brent spread is calculated each day. If the current spread value is above SMA 20, then we enter a short position in the spread on close (betting that the spread will decrease to the fair value represented by SMA 20). The trade is closed at the close of the trading day when the spread crosses below fair value. If the current spread value is below SMA 20, then we enter a long position betting that the spread will increase, and the trade is closed at the close of the trading day when the spread crosses above fair value.
Hedge for stocks during bear markets
Unknown – The source and related research papers don't offer insight into the correlation structure of trading strategy to equity market risk; therefore, we do not know if this strategy can be used as a hedge/diversification during the time of market crisis. Commodities usually have a negative correlation to equities; therefore, the proposed strategy can be negatively correlated, too, but a rigorous backtest is needed to asses if this is the case ...
Out-of-sample strategy's implementation/validation in QuantConnect's framework(chart, statistics & code)
Source paper
Evans, Dunis, Laws: Trading Futures Spread: An Application of Correlation
Abstract: Original motivation for this paper is the investigation of a correlation filter to improve the risk/return performance of the trading models. Further motivation is to extend the trading of futures spreads past the “Fair Value” type of model used by Butterworth and Holmes (2003). The trading models tested are the following; the cointegration “fair value” approach, MACD, traditional regression techniques and Neural Network Regression. Also shown is the effectiveness of the two types of filter, a standard filter and a correlation filter on the trading rule returns. Our results show that the best model for trading the WTI-Brent spread is an ARMA model, which proved to be profitable, both in- and out-of-sample. This is shown by out-of-sample annualised returns of 34.94% for the standard and correlation filters alike (inclusive of transactions costs).
Other papers
Lubnau: Spread trading strategies in the crude oil futures market
Abstract: his article explores whether common technical trading strategies used in equity markets can be employed profitably in the markets for WTI and Brent crude oil. The strategies tested are Bollinger Bands, based on a mean-reverting hedge portfolio of WTI and Brent. The trading systems are tested with historical data from 1992 to 2013, representing 22 years of data and for various specifications. The hedge ratio for the crude oil portfolio is derived by using the Johansen procedure and a dynamic linear model with Kalman filtering. The significance of the results is evaluated with a bootstrap test in which randomly generated orders are employed. Results show that some setups of the system are able to be profitable over every five-year period tested. Furthermore they generate profits and Sharpe ratios that are significantly higher than those of randomly generated orders of approximately the same holding time. The best results with some Sharpe ratios in excess of three, are obtained when a dynamic linear model with Kalman filtering and maximum likelihood estimates of the unknown variance of the state equation is employed to constantly update the hedge ratio of the portfolio. The results indicate that the crude oil market may not be weak-form efficient.
Donninger: The Poverty of Academic Finance Research: Spread Trading Strategies in the Crude Oil Futures Market
Abstract: Harvey, Liu and Zhu argue that probably most of the Cross-Section of Returns literature is garbage. One can always try an additional factor and will find a significant Cross-Sectional result with enough trial and error. Lopez de Prado argues in a series of articles in a similar vein. Theoretically scientific results are falsifiable. Practically previous results and publications are checked only in rare occasions. Growth in a Time of Depth by Reinhart-Rogoff was the most influential economic paper in recent years. It was published in a top journal. Although the paper contained even trivial Excel-Bugs it took 3 years till the wrong results and the poor methodology was fully revealed. The reviewers did not check the simple spreadsheets. This paper analyzes a less prominent example about spread trading in the crude oil futures market by Thorben Lubnau. The author reports for his very simple strategy a long term Sharpe-Ratios above 3. It is shown that - like for Reinhart-Rogoff - one needs no sophisticated test statistics to falsify the results. The explanation is much simpler: The author has no clue of trading. He used the wrong data.
Fanelli, Viviana and Fontana, Claudio and Rotondi, Francesco: 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.