Conditionally heteroscedastic factorial HMMs for time series in finance

In this article, we develop a new approach within the framework of asset pricing models that incorporates two key features of the latent volatility: co‐movement among conditionally heteroscedastic financial returns and switching between different unobservable regimes. By combining latent factor mode...

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Vydané v:Applied stochastic models in business and industry Ročník 23; číslo 6; s. 503 - 529
Hlavní autori: Saidane, Mohamed, Lavergne, Christian
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Chichester, UK John Wiley & Sons, Ltd 01.11.2007
Wiley
Predmet:
dynamic factor analysis
EM algorithm
finance
GPB approximation
GQARCH processes
HMM
Mathematics
Methodology
Statistics
Statistics Theory
switching Kalman filter
ISSN:1524-1904, 1526-4025
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Shrnutí:In this article, we develop a new approach within the framework of asset pricing models that incorporates two key features of the latent volatility: co‐movement among conditionally heteroscedastic financial returns and switching between different unobservable regimes. By combining latent factor models with hidden Markov chain models we derive a dynamical local model for segmentation and prediction of multivariate conditionally heteroscedastic financial time series. We concentrate more precisely on situations where the factor variances are modelled by univariate generalized quadratic autoregressive conditionally heteroscedastic processes. The expectation maximization algorithm that we have developed for the maximum likelihood estimation is based on a quasi‐optimal switching Kalman filter approach combined with a generalized pseudo‐Bayesian approximation, which yield inferences about the unobservable path of the common factors, their variances and the latent variable of the state process. Extensive Monte Carlo simulations and preliminary experiments obtained with daily foreign exchange rate returns of eight currencies show promising results. Copyright © 2007 John Wiley & Sons, Ltd.
Bibliografia:istex:63456E04751716CBD8048DE0C2E0BAD64DA8D6A8
ark:/67375/WNG-RZ9QG6Z9-C
ArticleID:ASMB687
ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1524-1904
1526-4025
DOI:10.1002/asmb.687