Modelling financial time series with switching state space models

Mehdi Azzouzi, Ian T. Nabney

Research output: Chapter in Book/Report/Conference proceedingChapter in a book

4 Citations (Scopus)
11 Downloads (Pure)

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 [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.
Original languageEnglish
Title of host publicationIEEE/IAFE Conference on Computational Intelligence for Financial Engineering, Proceedings (CIFEr)
Place of PublicationUnited States
PublisherIEEE Computer Society
Pages240-249
Number of pages10
Publication statusPublished - 1 Jan 1999

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