Pairs trading with partial cointegration
提出部分协整模型用于配对交易，通过状态空间表示和最大似然估计识别交易信号，在标普500数据上实现年化超12%的收益，优于传统方法。
Partial cointegration is a weakening of cointegration that allows for the ‘cointegrating’ residual to contain a random walk and a mean-reverting component. We derive its representation in state space, provide a maximum likelihood-based estimation routine, and a suitable likelihood ratio test. Then, we explore the use of partial cointegration as a means for identifying promising pairs and for generating buy and sell signals. Specifically, we benchmark partial cointegration against several classical pairs trading variants from 1990 until 2015, on a survivor bias free data-set of the S&P 500 constituents. We find annualized returns of more than 12% after transaction costs. These results can only partially be explained by common sources of systematic risk and are well superior to classical distance-based or cointegration-based pairs trading variants on our data-set.