Dynamic modeling of mean-reverting spreads for statistical arbitrage
Statistical 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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Volume (Year): 8 (2011)
Issue (Month): 1 (April)
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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Peter C.B. Phillips & Sam Ouliaris, 1987.
"Asymptotic Properties of Residual Based Tests for Cointegration,"
Cowles Foundation Discussion Papers
847R, Cowles Foundation for Research in Economics, Yale University, revised Jul 1988.
- Phillips, Peter C B & Ouliaris, S, 1990. "Asymptotic Properties of Residual Based Tests for Cointegration," Econometrica, Econometric Society, vol. 58(1), pages 165-93, January.
- Kadane, Joseph B. & Chan, Ngai Hang & Wolfson, Lara J., 1996. "Priors for unit root models," Journal of Econometrics, Elsevier, vol. 75(1), pages 99-111, November.
- Monahan, John F., 1983. "Fully Bayesian analysis of ARMA time series models," Journal of Econometrics, Elsevier, vol. 21(3), pages 307-331, April.
- James M. Poterba & Lawrence H. Summers, 1987.
"Mean Reversion in Stock Prices: Evidence and Implications,"
NBER Working Papers
2343, National Bureau of Economic Research, Inc.
- Poterba, James M. & Summers, Lawrence H., 1988. "Mean reversion in stock prices : Evidence and Implications," Journal of Financial Economics, Elsevier, vol. 22(1), pages 27-59, October.
- Chaudhuri, Kausik & Wu, Yangru, 2003. "Random walk versus breaking trend in stock prices: Evidence from emerging markets," Journal of Banking & Finance, Elsevier, vol. 27(4), pages 575-592, April.
- Kadiyala, K. Rao & Karlsson, Sune, 1994.
"Numerical Aspects of Bayesian VAR-modeling,"
SSE/EFI Working Paper Series in Economics and Finance
12, Stockholm School of Economics.
- Kadiyala, K Rao & Karlsson, Sune, 1997. "Numerical Methods for Estimation and Inference in Bayesian VAR-Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(2), pages 99-132, March-Apr.
- Giovanni Montana & Kostas Triantafyllopoulos & Theodoros Tsagaris, 2007. "Flexible least squares for temporal data mining and statistical arbitrage," Papers 0709.3884, arXiv.org.
- M. Ruth & K. Donaghy & P. Kirshen, 2006. "Introduction," Chapters, in: Regional Climate Change and Variability, chapter 1 Edward Elgar Publishing.
- Paul L. Anderson & Mark M. Meerschaert, 2005. "Parameter Estimation for Periodically Stationary Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(4), pages 489-518, 07.
- Kalaba, Robert E. & Tesfatsion, Leigh S., 1988.
"The Flexible Least Squares Approach to Time-Varying Linear Regression,"
Staff General Research Papers Archive
11198, Iowa State University, Department of Economics.
- Kalaba, Robert & Tesfatsion, Leigh, 1988. "The flexible least squares approach to time-varying linear regression," Journal of Economic Dynamics and Control, Elsevier, vol. 12(1), pages 43-48, March.
- Fama, Eugene F & French, Kenneth R, 1988. "Permanent and Temporary Components of Stock Prices," Journal of Political Economy, University of Chicago Press, vol. 96(2), pages 246-73, April.
- repec:spr:pharme:v:22:y:2004:i:4:p:225-244 is not listed on IDEAS
- Francq, C. & Zakoian, J. -M., 2001.
"Stationarity of multivariate Markov-switching ARMA models,"
Journal of Econometrics,
Elsevier, vol. 102(2), pages 339-364, June.
- Christian Francq & Jean-Michel Zakoïan, 2000. "Stationarity of Multivariate Markov-Switching ARMA Models," Working Papers 2000-32, Centre de Recherche en Economie et Statistique.
- Carcano, G. & Falbo, P. & Stefani, S., 2005. "Speculative trading in mean reverting markets," European Journal of Operational Research, Elsevier, vol. 163(1), pages 132-144, May.
- Ni, Shawn & Sun, Dongchu, 2003. "Noninformative priors and frequentist risks of bayesian estimators of vector-autoregressive models," Journal of Econometrics, Elsevier, vol. 115(1), pages 159-197, July.
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