Modelling Financial Time Series with Switching State Space Models - Computational Intelligence for Financial Engineering, 1999. (CIFEr). Proceedings of the IEEE/IAFE

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 trainiig such models with a variational EM algorithm due to [Ghahramani and Hinton, 19981. 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.