Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/178766
Authors: 
Endres, Sylvia
Stübinger, Johannes
Year of Publication: 
2018
Series/Report no.: 
FAU Discussion Papers in Economics 07/2018
Abstract: 
This paper develops the regime classification algorithm and applies it within a fully-edged pairs trading framework on minute-by-minute data of the S&P 500 constituents from 1998 to 2015. Specifically, the highly flexible algorithm automatically determines the number of regimes for any stochastic process and provides a complete set of parameter estimations. We demonstrate its performance in a simulation study - the algorithm achieves promising results for the general class of Lévy-driven Ornstein-Uhlenbeck processes with regime switches. In our empirical back-testing study, we apply our regime classification algorithm to propose a high-frequency pair selection and trading strategy. The results show statistically and economically significant returns with an annualized Sharpe ratio of 3.92 after transaction costs - results remain stable even in recent years. We compare our strategy with existing quantitative trading frameworks and find its results to be superior in terms of risk and return characteristics. The algorithm takes full advantage of its flexibility and identifies various regime patterns over time that are well-documented in the literature.
Subjects: 
Finance
Pairs trading
Statistical arbitrage
Markov regime switching
Lévy-driven Ornstein-Uhlenbeck process
High-frequency data
Document Type: 
Working Paper
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