Bayesian analysis of multivariate stochastic volatility and dynamic models

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.

Name thesis:degree_name

Ph. D.

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Statistics (MU)

Grantor dc:publisher

University of Missouri--Columbia

Year dc:date.issued

2006

Author dc:creator

• Loddo, Antonello, 1976-

Advisor dc:contributor.advisor

• Sun, Dongchu

Statement dc:rights

• OpenAccess.

Language dc:language.iso

eng, English

DOI

https://doi.org/10.32469/10355/4359

OAI identifier oai:identifier

oai:mospace.umsystem.edu:10355/4359