Demonstrating error-correction modelling for intraday statistical arbitrage

Applying cointegration analysis to security price movements illustrates how securities move together in the long-term. This can be augmented with an error-correction model to show how the long-run relationship is approached when the security prices are out of line with their cointegrated relationship. Cointegration and error-correction modelling promises to be useful in statistical arbitrage applications: not only does it show what relative prices of securities should be, but it also illuminates the short-run dynamics of how equilibrium should be restored along with how long it will take. Cointegration, coupled with error-correction modelling, promises to be a profitable way of implementing statistical arbitrage strategies.1 Bondarenko (2003) and Hogan et al. (2004) defined statistical arbitrage as an attempt to exploit the long-horizon trading opportunities revealed by cointegration relationships. Alexander and Dimitriu (2005) showed how cointegration is a better way of implementing a statistical arbitrage strategy than other conventional ways, like the use of tracking error variance minimization. These previous studies, however, did not add error-correction modelling to the trading strategies. This article seeks to fill that gap, by presenting how to implement a statistical arbitrage strategy based on cointegration and error-correction modelling.