This study derives an optimal pairs trading strategy based on a Lévy-driven Ornstein-Uhlenbeck process and applies it to high-frequency data of the S&P 500 constituents from1998 to 2015. Our model provides optimal entry and exit signals by maximizing the expected return expressed in terms of...

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...

This paper develops a pairs trading framework based on a mean-reverting jump-diffusion model and applies it to minute-by-minute data of the S&P 500 oil companies from 1998 to 2015. The established statistical arbitrage strategy enables us to perform intraday and overnight trading. Essentially,...