Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models
N/D
Autores
Orientador
Co-orientadores
Citações na Scopus
Tipo de documento
Working Paper
Data
2016
Título da Revista
ISSN da Revista
Título do Volume
Resumo
We discuss efficient Bayesian estimation of dynamic covariance matrices in multivariate time series through a factor stochastic volatility model. In particular, we propose two interweaving strategies (Yu and Meng, 2011) to substantially accelerate convergence and mixing of standard MCMC approaches. Similar to marginal data augmentation techniques, the proposed acceleration procedures exploit non-identifiability issues which frequently arise in factor models. Our new interweaving strategies are easy to implement and come at almost no extra computational cost; nevertheless, they can boost estimation efficiency by several orders of magnitude as is shown in extensive simulation studies. To conclude, the application of our algorithm to a 26-dimensional exchange rate data set illustrates the superior performance of the new approach for real-world data.
Titulo de periódico
URL da fonte
Título de Livro
URL na Scopus
Idioma
Inglês
URL permanente
Notas
Membros da banca
Área do Conhecimento CNPQ
Ciências Exatas e da Terra