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Demonstrating error-correction modelling for intraday statistical arbitrage

Listed author(s):
  • Brian Jacobsen
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    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.

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    Article provided by Taylor and Francis Journals in its journal Applied Financial Economics Letters.

    Volume (Year): 4 (2008)
    Issue (Month): 4 ()
    Pages: 287-292

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    Handle: RePEc:taf:apfelt:v:4:y:2008:i:4:p:287-292
    DOI: 10.1080/17446540701720550
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