Abstract
We present an analysis of time-series modelling which allows for the possibility of an “unmodelled” component which is present in the underlying generating process, but is not captured in a particular “model” of the time-series. Within this framework, “nonstationarity” is a relative, rather than an absolute, property, which is conditional on a given data representation, model and/or set of parameters. We apply this perspective to the problem of trading statistical arbitrage models which are based on the econometric notion of weak cointegration between asset prices. We show how a modelling framework which supports multiple models may reduce the out-of-sample performance degradation which is caused by non-stationarities in the relative price dynamics of the set of target assets. A necessary condition is shown to be that the unmodelled components of the individual models must be less than perfectly correlated, thus motivating the use of a population-based algorithm which jointly optimises a portfolio of decorrelated models. We describe an application of this methodology to trading statistical arbitrage between equity index futures and present empirical results, before concluding with a brief discussion of the issues raised and an outline of ongoing developments.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Breiman L., Friedman J. H., Olshen R. A., and Stone C. J., 1984, Classification and Regression Trees, Wadsworth and Brooks/Cole, Monterey.
Burgess A. N., 1995, Non-linear model identification and statistical significance tests and their application to financial modelling, in IEE Proceedings of the 4th International Conference on Artificial Neural Networks, Cambridge, 312–317
Burgess A. N., 1996, Statistical yield curve arbitrage in eurodollar futures using neural networks, in Refenes et al (eds), Neural Networks in Financial Engineering, World Scientific, Singapore, 98–110
Burgess A. N., 1997, Asset allocation across European equity indices using a portfolio of dynamic cointegration models, in Weigend et al (eds) Neural Networks in Financial Engineering, World-Scientific, Singapore, 276–288
Burgess A. N., 1998, A new perspective on nonstationarity, Technical Report, Decision Technology Centre, London Business School
Burgess A. N. and Refenes A. N., 1996, Modelling non-linear cointegration in international equity index futures, in Refenes et al (eds), Neural Networks in Financial Engineering, World Scientific, Singapore, 50–63
Dickey D. A. and Fuller W. A., 1979, Distribution of the estimators for autoregressive time-series with a unit root, Journal of the American Statistical Association, 74, 427–431
Engle R. F. and Granger C. W. J., 1987, Co-integration and error-correction: representation,estimation and testing, Econometrica, 55, 251–276
Friedman J.H. and Stuetzle W., 1981. Projection pursuit regression. Journal of the American Statistical Association. Vol. 76, pp. 817–823.
Granger C. W. J., 1983, Co-integrated variables and error-correcting models, UCSD Discussion Paper.
Hastie T.J. and Tibshirani R.J., 1990. Generalised Additive Models. Chapman and Hall, London
Jacobs R. A., Jordan M. I., Nowlan S. J. and Hinton G. E., 1991, Adaptive mixtures of local experts, Neural Computation, 3, 79–87
Lo A. W., and MacKinley A. C., 1988, Stock market prices do not follow random walks: evidence from a simple specification test, The Review of Financial Studies, Vol 1, Number 1, pp. 41–66
Refenes A. N., Bentz Y. and Burgess N., 1994, Neural networks in investment management, Journal of Communications and Finance, 8, April 95–101
Rehfuss S., Wu L., and Moody J. E., 1996, Trading using committees: A comparative study, in Refenes et al (eds), Neural Networks in Financial Engineering, World Scientific, Singapore, 612–621
Weigend A. S., and Mangeas M., 1996, Analysis and prediction of multistationary time series, in Refenes et al (eds), Neural Networks in Financial Engineering, World Scientific, Singapore, 597–611
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1998 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Burgess, A.N. (1998). Controlling Nonstationarity in Statistical Arbitrage Using a Portfolio of Cointegration Models. In: Refenes, AP.N., Burgess, A.N., Moody, J.E. (eds) Decision Technologies for Computational Finance. Advances in Computational Management Science, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5625-1_7
Download citation
DOI: https://doi.org/10.1007/978-1-4615-5625-1_7
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-7923-8309-3
Online ISBN: 978-1-4615-5625-1
eBook Packages: Springer Book Archive