Dynamic modeling of mean-reverting spreads for statistical arbitrage
AbstractStatistical arbitrage strategies, such as pairs trading and its generalizations, rely on the construction of mean-reverting spreads enjoying a certain degree of predictability. Gaussian linear state-space processes have recently been proposed as a model for such spreads under the assumption that the observed process is a noisy realization of some hidden states. Real-time estimation of the unobserved spread process can reveal temporary market inefficiencies which can then be exploited to generate excess returns. Building on previous work, we embrace the state-space framework for modeling spread processes and extend this methodology along three different directions. First, we introduce time-dependency in the model parameters, which allows for quick adaptation to changes in the data generating process. Second, we provide an on-line estimation algorithm that can be constantly run in real-time. Being computationally fast, the algorithm is particularly suitable for building aggressive trading strategies based on high-frequency data and may be used as a monitoring device for mean-reversion. Finally, our framework naturally provides informative uncertainty measures of all the estimated parameters. Experimental results based on Monte Carlo simulations and historical equity data are discussed, including a co-integration relationship involving two exchange-traded funds.
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Bibliographic InfoArticle provided by Springer in its journal Computational Management Science.
Volume (Year): 8 (2011)
Issue (Month): 1 (April)
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Web page: http://www.springerlink.com/link.asp?id=111894
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- Kostas Triantafyllopoulos & Giovanni Montana, 2008. "Dynamic modeling of mean-reverting spreads for statistical arbitrage," Papers 0808.1710, arXiv.org, revised May 2009.
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