Bayesian Non-Parametric Mixtures of GARCH(1,1) Models
Joint Authors
Source
Journal of Probability and Statistics
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-07-16
Country of Publication
Egypt
No. of Pages
16
Main Subjects
Abstract EN
Traditional GARCH models describe volatility levels that evolve smoothly over time, generated by a single GARCH regime.
However, nonstationary time series data may exhibit abrupt changes in volatility, suggesting changes in the underlying GARCH regimes.
Further, the number and times of regime changes are not always obvious.
This article outlines a nonparametric mixture of GARCH models that is able to estimate the number and time of volatility regime changes by mixing over the Poisson-Kingman process.
The process is a generalisation of the Dirichlet process typically used in nonparametric models for time-dependent data provides a richer clustering structure, and its application to time series data is novel.
Inference is Bayesian, and a Markov chain Monte Carlo algorithm to explore the posterior distribution is described.
The methodology is illustrated on the Standard and Poor's 500 financial index.
American Psychological Association (APA)
Lau, John W.& Cripps, Ed. 2012. Bayesian Non-Parametric Mixtures of GARCH(1,1) Models. Journal of Probability and Statistics،Vol. 2012, no. 2012, pp.1-16.
https://search.emarefa.net/detail/BIM-451224
Modern Language Association (MLA)
Lau, John W.& Cripps, Ed. Bayesian Non-Parametric Mixtures of GARCH(1,1) Models. Journal of Probability and Statistics No. 2012 (2012), pp.1-16.
https://search.emarefa.net/detail/BIM-451224
American Medical Association (AMA)
Lau, John W.& Cripps, Ed. Bayesian Non-Parametric Mixtures of GARCH(1,1) Models. Journal of Probability and Statistics. 2012. Vol. 2012, no. 2012, pp.1-16.
https://search.emarefa.net/detail/BIM-451224
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-451224