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Journal of Statistical Theory and Practice

, Volume 2, Issue 4, pp 597–632 | Cite as

Improved Nonlinear Multivariate Financial Time Series Prediction with Mixed-State Latent Factor Models

  • Mohamed SaidaneEmail author
  • Christian Lavergne
Article
  • 2 Downloads

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 conditionally heteroskedastic latent factor model in a hybrid mixed-state latent factor model (MSFM) and discuss the practical details of training such models with a new approximated version of the Viterbi algorithm in conjunction with the expectation-maximization (EM) algorithm to iteratively estimate the model parameters in a maximum-likelihood sense. The performance of the MSFM is evaluated on both simulated and financial data sets. On the basis of out-of-sample forecast encompassing tests as well as other measures for forecasting accuracy, our results indicate that the use of this new method yields overall better forecasts than those generated by competing models.

Key-words

Latent factor models EM algorithm Conditional heteroskedasticity HMM Time series segmentation Forecasting 

AMS Subject Classification

62H25 62M05 62M10 62P20 

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Copyright information

© Grace Scientific Publishing 2008

Authors and Affiliations

  1. 1.The University of 7 November at Carthage, ISCC BizerteZarzounaTunisia
  2. 2.University of Montpellier 2, UMR-CNRS 5149France

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