Title :
Modelling financial time series with switching state space models
Author :
Azzouzi, Mehdi ; Nabney, Ian T.
Author_Institution :
Neural Comput. Res. Group, Aston Univ., Birmingham, UK
Abstract :
The deficiencies of stationary models applied to financial time series are well documented. A special form of non-stationarity, where the underlying generator switches between (approximately) stationary regimes, seems particularly appropriate for financial markets. We use a dynamic switching (modelled by a hidden Markov model) combined with a linear dynamical system in a hybrid switching state space model (SSSM) and discuss the practical details of training such models with a variational EM algorithm due to Z. Ghahramani and G.E. Hinton (1998). The performance of the SSSM is evaluated on several financial data sets and it is shown to improve on a number of existing benchmark methods
Keywords :
financial data processing; hidden Markov models; state-space methods; time series; variational techniques; SSSM; benchmark methods; dynamic switching; financial data sets; financial markets; financial time series modelling; hidden Markov model; hybrid switching state space model; linear dynamical system; non-stationarity; stationary models; stationary regimes; switching state space models; underlying generator; variational EM algorithm; Control engineering; Data engineering; Econometrics; Economic forecasting; Exchange rates; Hidden Markov models; Jacobian matrices; Predictive models; State-space methods; Switches;
Conference_Titel :
Computational Intelligence for Financial Engineering, 1999. (CIFEr) Proceedings of the IEEE/IAFE 1999 Conference on
Conference_Location :
New York, NY
Print_ISBN :
0-7803-5663-2
DOI :
10.1109/CIFER.1999.771123
