Modelling financial time series with switching state space models


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 Hilton,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.

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Divisions: ?? 50811700Jl ??
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: ©1999 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Event Title: IEEE/IAFE 1999 Conference on Computational Intelligence for Financial Engineering
Event Type: Other
Event Dates: 1999-01-01 - 1999-01-01
Uncontrolled Keywords: deficiencies,stationary models,financial time series,non-stationarity,stationary regimes,generator switches,financial markets,hidden Markov model,financial data sets,benchmark methods
ISBN: 0780356632
Last Modified: 27 Jun 2024 12:04
Date Deposited: 15 Sep 2009 13:09
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Related URLs: http://ieeexplo ... arnumber=771123 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Publisher URL)
PURE Output Type: Chapter
Published Date: 1999
Authors: Azzouzi, Mehdi
Nabney, Ian T. (ORCID Profile 0000-0003-1513-993X)



Version: Published Version

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