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 [Ghahramani and 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.
|Title of host publication||IEEE/IAFE Conference on Computational Intelligence for Financial Engineering, Proceedings (CIFEr)|
|Place of Publication||United States|
|Publisher||IEEE Computer Society|
|Number of pages||10|
|Publication status||Published - 1 Jan 1999|