Incorporating volatility in tolerance intervals for pair-trading strategy and backtesting

Cathy W. S. Chen, Tsai-Yu Lin and T. Y. Huang

Abstract

Return volatility plays a crucial role in securities trading. This paper incorporates volatility forecasting via the exponentially weighted moving average model into traditional tolerance limits for pair-trading strategies, which we call the semiparametric version of the tolerance interval. We illustrate how the proposed method helps uncover arbitrage opportunities via the daily return spreads of fifteen pairs of artificial intelligence stocks in the US equity markets. This study compares the backtesting performance of the proposed pair-trading strategy with individual stocks and the traditional tolerance interval strategy over three semiannual periods from July 2016 to December 2017. The results show that the proposed trading strategy is the most profitable one, as opposed to investing in individual stocks or employing the traditional tolerance interval strategy.