Bayesian Non-Parametric Mixtures of GARCH(1,1) Models
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 change...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2012-01-01
|
| Series: | Journal of Probability and Statistics |
| Online Access: | http://dx.doi.org/10.1155/2012/167431 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832555552902742016 |
|---|---|
| author | John W. Lau Ed Cripps |
| author_facet | John W. Lau Ed Cripps |
| author_sort | John W. Lau |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9df7c8f626084abab4e3a0426600727f |
| institution | Kabale University |
| issn | 1687-952X 1687-9538 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Probability and Statistics |
| spelling | doaj-art-9df7c8f626084abab4e3a0426600727f2025-02-03T05:47:52ZengWileyJournal of Probability and Statistics1687-952X1687-95382012-01-01201210.1155/2012/167431167431Bayesian Non-Parametric Mixtures of GARCH(1,1) ModelsJohn W. Lau0Ed Cripps1School of Mathematics and Statistics, The University of Western Australia, Perth, AustraliaSchool of Mathematics and Statistics, The University of Western Australia, Perth, AustraliaTraditional 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.http://dx.doi.org/10.1155/2012/167431 |
| spellingShingle | John W. Lau Ed Cripps Bayesian Non-Parametric Mixtures of GARCH(1,1) Models Journal of Probability and Statistics |
| title | Bayesian Non-Parametric Mixtures of GARCH(1,1) Models |
| title_full | Bayesian Non-Parametric Mixtures of GARCH(1,1) Models |
| title_fullStr | Bayesian Non-Parametric Mixtures of GARCH(1,1) Models |
| title_full_unstemmed | Bayesian Non-Parametric Mixtures of GARCH(1,1) Models |
| title_short | Bayesian Non-Parametric Mixtures of GARCH(1,1) Models |
| title_sort | bayesian non parametric mixtures of garch 1 1 models |
| url | http://dx.doi.org/10.1155/2012/167431 |
| work_keys_str_mv | AT johnwlau bayesiannonparametricmixturesofgarch11models AT edcripps bayesiannonparametricmixturesofgarch11models |