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.

Divisions: ?? 50811700Jl ??
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
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Event Title: IEEE/IAFE 1999 Conference on Computational Intelligence for Financial Engineering
Event Type: Other
Event Dates: 1999-01-01 - 1999-01-01
ISBN: 0780356632
Last Modified: 29 Nov 2023 13:43
Date Deposited: 15 Sep 2009 13:09
Full Text Link: 10.1109/CIFER.1999.771123
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.



Version: Published Version

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