Artículos relacionados a BAYESIAN STOCHASTIC VOLATILITY MODELS: AUXILIARY VARIABLE...

BAYESIAN STOCHASTIC VOLATILITY MODELS: AUXILIARY VARIABLE METHODS FOR STOCHASTIC VOLATILITY AND OTHER TIME-VARYING VOLATILITY MODELS - Tapa blanda

Giakoumatos, Stefanos

 
9783838386331: BAYESIAN STOCHASTIC VOLATILITY MODELS: AUXILIARY VARIABLE METHODS FOR STOCHASTIC VOLATILITY AND OTHER TIME-VARYING VOLATILITY MODELS
Tapa blanda
ISBN 10: 3838386337 ISBN 13: 9783838386331
Editorial: LAP LAMBERT Academic Publishing, 2010

Sinopsis

The phenomenon of changing variance and covariance is often encountered in financial time series. As a result, during the last years researchers focused on the time-varying volatility models. These models are able to describe the main characteristics of the financial data such as the volatility clustering. In addition, the development of the Markov Chain Monte Carlo Techniques (MCMC) provides a powerful tool for the estimation of the parameters of the time-varying volatility models, in the context of Bayesian analysis. In this thesis, we adopt the Bayesian inference and we propose easy-to-apply MCMC algorithms for a variety of time-varying volatility models. We use a recent development in the context of the MCMC techniques, the Auxiliary variable sampler. This technique enables us to construct MCMC algorithms, which only consist of Gibbs steps. We propose new MCMC algorithms for many univariate and multivariate models. Furthermore, we apply the proposed MCMC algorithms to real data and compare the above models based on their predictive distribution

"Sinopsis" puede pertenecer a otra edición de este libro.

Reseña del editor

The phenomenon of changing variance and covariance is often encountered in financial time series. As a result, during the last years researchers focused on the time-varying volatility models. These models are able to describe the main characteristics of the financial data such as the volatility clustering. In addition, the development of the Markov Chain Monte Carlo Techniques (MCMC) provides a powerful tool for the estimation of the parameters of the time-varying volatility models, in the context of Bayesian analysis. In this thesis, we adopt the Bayesian inference and we propose easy-to-apply MCMC algorithms for a variety of time-varying volatility models. We use a recent development in the context of the MCMC techniques, the Auxiliary variable sampler. This technique enables us to construct MCMC algorithms, which only consist of Gibbs steps. We propose new MCMC algorithms for many univariate and multivariate models. Furthermore, we apply the proposed MCMC algorithms to real data and compare the above models based on their predictive distribution

Biografía del autor

Stefanos Giakoumatos is Associate Professor of Statistics and Quantitative Methods in the Technological Educational Institute of Kalamata Greece. He was born in Athens in 1972 and he was studied Statistics in the Athens University of Economics and Business (Ph.D, M.Sc., B.Sc). His scientific interests include MCMC methods, Time-series Analysis.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
LAP LAMBERT Academic Publishing
Año de publicación
2010
Idioma
Inglés
ISBN 10 
3838386337
ISBN 13 
9783838386331
Encuadernación
Tapa blanda
Número de páginas
240
Contacto del fabricante
no disponible
Persona responsable
no disponible