Bayesian analysis of multivariate stochastic volatility and dynamic models

MOspace/Manakin Repository

Breadcrumbs Navigation

Bayesian analysis of multivariate stochastic volatility and dynamic models

Please use this identifier to cite or link to this item: http://hdl.handle.net/10355/4359

[-] show simple item record

dc.contributor.advisor Sun, Dongchu en
dc.contributor.author Loddo, Antonello, 1976- en_US
dc.date.accessioned 2010-01-12T17:05:42Z
dc.date.available 2010-01-12T17:05:42Z
dc.date.issued 2006 en_US
dc.date.submitted 2006 Summer en
dc.identifier.other LoddoA-070306-D5755 en_US
dc.identifier.uri http://hdl.handle.net/10355/4359
dc.description The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. en_US
dc.description Title from title screen of research.pdf file viewed on (April 26, 2007) en_US
dc.description Includes bibliographical references. en_US
dc.description Vita. en_US
dc.description Thesis (Ph.D.) University of Missouri-Columbia 2006. en_US
dc.description Dissertations, Academic -- University of Missouri--Columbia -- Statistics. en_US
dc.description.abstract We consider a multivariate regression model with time varying volatilities in the error term. The time varying volatility for each component of the error is of unknown nature, may be deterministic or stochastic. We propose Bayesian stochastic search as a feasible variable selection technique for the regression and volatility equations. We develop Markov Chain Monte Carlo (MCMC) algorithms that generate a posteriori restrictions on the elements of both the regression coefficients and the covariance matrix of the error term. Efficient parametrization of the time varying covariance matrices is studied using different modified Cholesky decompositions. We propose a hierarchal approach for selection of the volatility equation's variance components. We extend the results of the first in order to apply the stochastic search algorithm to dynamic model settings. We develop a MCMC algorithm that performs a stochastic model selection for the coefficients and the covariance matrix of the latent process of a dynamic model, thus making the choice of the best model only based on probabilistic considerations. en_US
dc.language.iso en_US en_US
dc.publisher University of Missouri--Columbia en_US
dc.relation.ispartof 2006 Freely available dissertations (MU) en_US
dc.subject.lcsh Bayesian statistical decision theory en_US
dc.subject.lcsh Markov processes en_US
dc.subject.lcsh Monte Carlo method en_US
dc.title Bayesian analysis of multivariate stochastic volatility and dynamic models en_US
dc.type Thesis en_US
thesis.degree.discipline Statistics en_US
thesis.degree.grantor University of Missouri--Columbia en_US
thesis.degree.name Ph.D. en_US
thesis.degree.level Doctoral en_US
dc.identifier.merlin .b5847089x en_US
dc.identifier.oclc 123570453 en_US
dc.relation.ispartofcommunity University of Missouri-Columbia. Graduate School. Theses and Dissertations. Dissertations. 2006 Dissertations


This item appears in the following Collection(s)

[-] show simple item record