Statistical arbitrage, or sometimes called pairs trading, is an investment strategy whichexploits the historical price relationships between two or several assets and profits fromrelative mispricing. It has a long history in hedge fund industry and variates of this kindof strategies are still profitable nowadays. The idea is simple and the source of the profithas support from fundamentals in economics and pricing theories. However, there are stillmany difficulties in implementing and testing such strategies in real life, which includehow to select pairs, how to estimate hedge ratio, when to enter, when to exit and etc.Due to its proprietary nature, there is very few literature on this subject. This thesisis an attempt to demystify statistical arbitrage in high-frequency settings, using freelyavailable data of Chinese commodity futures. This thesis introduces and discusses theexisting research done on this subject. Also, with the help of advanced statistical inferenceapproaches for treating time series, this thesis proposed a new model which generalizesthe entire process of creating a profitable statistical arbitrage trading strategy for a givenmarket. Several different approaches are implemented and their simulated performancesin the Chinese commodity future market are compared horizontally. Unlike much otherexisting literature, transaction costs and market frictions have been considered thoroughlyin order to make the research result more meaningful. Empirical results show that our newmodel delivers very competitive performance in online hedge ratio estimation.
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High Frequency Statistical Arbitrage with Kalman Filter and Markov Chain Monte Carlo
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