Infinite-State Markov-Switching for Dynamic Volatility
Author
Abstract
Suggested Citation
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Fisher, Mark & Jensen, Mark J., 2019.
"Bayesian inference and prediction of a multiple-change-point panel model with nonparametric priors,"
Journal of Econometrics, Elsevier, vol. 210(1), pages 187-202.
- Mark Fisher & Mark J. Jensen, 2018. "Bayesian Inference and Prediction of a Multiple-Change-Point Panel Model with Nonparametric Priors," Working Paper series 18-12, Rimini Centre for Economic Analysis.
- Mark Fisher & Mark J. Jensen, 2018. "Bayesian Inference and Prediction of a Multiple-Change-Point Panel Model with Nonparametric Priors," FRB Atlanta Working Paper 2018-2, Federal Reserve Bank of Atlanta.
- Maciej Augustyniak & Mathieu Boudreault & Manuel Morales, 2018. "Maximum Likelihood Estimation of the Markov-Switching GARCH Model Based on a General Collapsing Procedure," Methodology and Computing in Applied Probability, Springer, vol. 20(1), pages 165-188, March.
- Yang, Qiao, 2019. "Stock returns and real growth: A Bayesian nonparametric approach," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 53-69.
- Yong Song & Tomasz Wo'zniak, 2020. "Markov Switching," Papers 2002.03598, arXiv.org.
- Li, Chenxing, 2022. "A multivariate GARCH model with an infinite hidden Markov mixture," MPRA Paper 112792, University Library of Munich, Germany.
- Chenxing Li & John M. Maheu & Qiao Yang, 2024.
"An infinite hidden Markov model with stochastic volatility,"
Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 2187-2211, September.
- Li, Chenxing & Maheu, John M & Yang, Qiao, 2022. "An Infinite Hidden Markov Model with Stochastic Volatility," MPRA Paper 115456, University Library of Munich, Germany.
- Martin, Gael M. & Frazier, David T. & Maneesoonthorn, Worapree & Loaiza-Maya, Rubén & Huber, Florian & Koop, Gary & Maheu, John & Nibbering, Didier & Panagiotelis, Anastasios, 2024.
"Bayesian forecasting in economics and finance: A modern review,"
International Journal of Forecasting, Elsevier, vol. 40(2), pages 811-839.
- Gael M. Martin & David T. Frazier & Worapree Maneesoonthorn & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2022. "Bayesian Forecasting in Economics and Finance: A Modern Review," Papers 2212.03471, arXiv.org, revised Jul 2023.
- Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
- Jin, Xin & Maheu, John M. & Yang, Qiao, 2022. "Infinite Markov pooling of predictive distributions," Journal of Econometrics, Elsevier, vol. 228(2), pages 302-321.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:oup:jfinec:v:14:y:2016:i:2:p:418-460.. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Oxford University Press (email available below). General contact details of provider: https://edirc.repec.org/data/sofieea.html .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.