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.
(This abstract was borrowed from another version of this item.)
Volume (Year): 8 (2011)
Issue (Month): 1 (April)
|Contact details of provider:|| Web page: http://www.springerlink.com/link.asp?id=111894|
|Order Information:||Web: http://link.springer.de/orders.htm|
References listed on IDEAS
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.:
- Giovanni Montana & Kostas Triantafyllopoulos & Theodoros Tsagaris, 2007. "Flexible least squares for temporal data mining and statistical arbitrage," Papers 0709.3884, arXiv.org.
- Christian Francq & Jean-Michel Zakoïan, 2000.
"Stationarity of Multivariate Markov-Switching ARMA Models,"
2000-32, Centre de Recherche en Economie et Statistique.
- Francq, C. & Zakoian, J. -M., 2001. "Stationarity of multivariate Markov-switching ARMA models," Journal of Econometrics, Elsevier, vol. 102(2), pages 339-364, June.
- M. Ruth & K. Donaghy & P. Kirshen, 2006. "Introduction," Chapters, in: Regional Climate Change and Variability, chapter 1 Edward Elgar Publishing.
- 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.
- Kalaba, Robert E. & Tesfatsion, Leigh S., 1988. "The Flexible Least Squares Approach to Time-Varying Linear Regression," Staff General Research Papers 11198, Iowa State University, Department of Economics.
- 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.
- 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.
- 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.
- 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.
- Manishi Prasad & Peter Wahlqvist & Rich Shikiar & Ya-Chen Tina Shih, 2004. "A," PharmacoEconomics, Springer Healthcare | Adis, vol. 22(4), pages 225-244.
- 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.
- Monahan, John F., 1983. "Fully Bayesian analysis of ARMA time series models," Journal of Econometrics, Elsevier, vol. 21(3), pages 307-331, April.
- 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.
- 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.
- 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.
- 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.
When requesting a correction, please mention this item's handle: RePEc:spr:comgts:v:8:y:2011:i:1:p:23-49. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sonal Shukla)or (Christopher F Baum)
If references are entirely missing, you can add them using this form.