Particle Filters for Markov-Switching Stochastic-Correlation Models
Gianni Amisano and
Roberto Casarin ()
Working Papers from University of Brescia, Department of Economics
This work deals with multivariate stochastic volatility models that account for time-varying stochastic correlation between the observable variables. We focus on the bivariate models. A contribution of the work is to introduce Beta and Gamma autoregressive processes for modelling the correlation dynamics. Another contribution f our work is to allow the parameter of the correlation process to be governed by a Markov-switching process. Finally we propose a simulation-based Bayesian approach, called regularised sequential Monte Carlo. This framework is suitable for on-line estimation and the model selection.
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11) Track citations by RSS feed
Downloads: (external link)
http://www.unibs.it/on-line/dse/Home/Ricerca/Paper ... o/documento9757.html
Our link check indicates that this URL is bad, the error code is: 404 Not Found (http://www.unibs.it/on-line/dse/Home/Ricerca/Paperdeldipartimento/documento9757.html [302 Found]--> https://www.unibs.it/on-line/dse/Home/Ricerca/Paperdeldipartimento/documento9757.html)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:ubs:wpaper:0814
Access Statistics for this paper
More papers in Working Papers from University of Brescia, Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Matteo Galizzi ().