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Bayesian analysis of mixture of autoregressive components with an application to financial market volatility
Author(s) -
Sampietro Stefano
Publication year - 2006
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.613
Subject(s) - autoregressive model , stochastic volatility , econometrics , volatility clustering , volatility (finance) , markov chain monte carlo , bayesian probability , star model , setar , mixture model , model selection , markov chain , computer science , economics , mathematics , autoregressive conditional heteroskedasticity , time series , autoregressive integrated moving average , statistics , artificial intelligence
In this paper, we present a fully Bayesian analysis of a finite mixture of autoregressive components. Neither the number of mixture components nor the autoregressive order of each component have to be fixed, since we treat them as stochastic variables. Parameter estimation and model selection are performed using Markov chain Monte Carlo methods. This analysis allows us to take into account the stationarity conditions on the model parameters, which are often ignored by Bayesian approaches. Finally, the application to return volatility of financial markets will be illustrated. Our model seems to be consistent with some empirical facts concerning volatility such as persistence, clustering effects, nonsymmetrical dependencies. Copyright © 2006 John Wiley & Sons, Ltd.