Dynamical local models for segmentation and prediction of financial time series

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Abstract

In the analysis and prediction of many real-world time series, the assumption of stationarity is not valid. A special form of non-stationarity, where the underlying generator switches between (approximately) stationary regimes, seems particularly appropriate for financial markets. We introduce a new model which combines a dynamic switching (controlled by a hidden Markov model) and a non-linear dynamical system. We show how to train this hybrid model in a maximum likelihood approach and evaluate its performance on both synthetic and financial data.

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  • NCRG_2000_009.pdf

    Rights statement: This is a preprint of an article submitted for consideration in the European Journal of Finance © 2001 copyright Taylor & Francis; European Journal of Finance is available online at: http://www.informaworld.com/openurl?genre=article&issn=1351-847X&volume=7&issue=4&spage=289

    437 KB, PDF-document

Details

Original languageEnglish
Pages (from-to)289-311
Number of pages23
JournalEuropean Journal of Finance
Volume7
Issue4
DOIs
StatePublished - 2001

Bibliographic note

This is a preprint of an article submitted for consideration in the European Journal of Finance © 2001 copyright Taylor & Francis; European Journal of Finance is available online at: http://www.informaworld.com/openurl?genre=article&issn=1351-847X&volume=7&issue=4&spage=289

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