Statistical Arbitrage in Commodity Futures Pricing

Statistical arbitrage in commodity futures pricing is a quantitative trading strategy based on historical price relationships, capturing spread regression opportunities by identifying statistical correlations between commodity futures. Implementing this strategy in MATLAB typically involves the following key steps: Data Preparation: First, acquire historical price data for commodity futures and perform data cleaning and preprocessing. Common data sources include Wind, Bloomberg, or exchange APIs. Ensure time alignment of data, handle missing values, and address outliers using MATLAB's data preprocessing functions like fillmissing and rmoutliers. Correlation Analysis: Screen for commodity pairs with long-term equilibrium relationships by calculating correlation coefficients or cointegration relationships between price series. Common methods include the Augmented Dickey-Fuller (ADF) Test and Johansen Cointegration Test, implementable through MATLAB's Econometrics Toolbox functions such as adftest and jcitest. Spread Modeling: Construct spread series (e.g., logarithmic price differences or linear combinations) for selected commodity pairs and determine trading trigger thresholds using statistical methods like mean reversion models or Bollinger Bands. For instance, generate arbitrage signals when the spread deviates from its historical mean by more than a certain standard deviation, calculable using MATLAB's movmean and movstd functions. Trading Signal Generation: Design long-short trading rules based on output from spread models. For example, short overvalued commodities and long undervalued ones when spreads widen, closing positions upon spread regression. MATLAB's Financial Toolbox can optimize signal logic and backtesting through functions like backtestEngine and strategy objects. Risk Management: Set stop-loss and take-profit conditions while monitoring performance metrics such as Sharpe ratio and maximum drawdown. MATLAB's backtesting framework helps evaluate strategy stability across different market conditions using performance analysis tools like sharpe and maxdrawdown. In practical applications, factors like transaction costs, slippage, and liquidity must be considered. Execution efficiency can be further enhanced through MATLAB's parallel computing capabilities or hybrid programming with C++/Python using MATLAB's integration features like MEX functions and Python interface.