Cointegration identification with metric learning

Abstract

Cointegration is an important topic for time series analysis, especially in finance pair trading and hedging area. Cointegration is a kind of structure in which a linear combination of two (or more) time series is stationary. Traditional way to identify cointegration is to use the OLS estimator, firstly run a regression and secondly run a unit root test on residuals. But such method is easy to lead to ambiguous and unstable result. Therefore, we developed a dimensionality reduction model based on automatically calculated common factors and adopted the Metric Learning method to find a method that can quickly reduce the dimensionality and test the cointegration relationship of stock pairs.