Dynamical local models for segmentation and prediction of financial time series

Azzouzi, M and Nabney, Ian T. (2001). Dynamical local models for segmentation and prediction of financial time series. European Journal of Finance, 7 (4), pp. 289-311.

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.

Publication DOI: https://doi.org/10.1080/13518470110071155
Divisions: Engineering & Applied Sciences > Computer Science
Engineering & Applied Sciences > Systems analytics research institute (SARI)
Additional Information: 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
Uncontrolled Keywords: NCRG
Full Text Link:
Related URLs: http://www.info ... sue=4&spage=289 (Publisher URL)
Published Date: 2001
Authors: Azzouzi, M
Nabney, Ian T. ( 0000-0003-1513-993X)

Download

[img]

Version: Accepted Version


Export / Share Citation


Statistics

Additional statistics for this record