Advanced search
Start date

Multivariate GARCH models with skewed distributions


The main objective of this project is to develop and apply stochastic simulation techniques in multivariate GARCH (Generalized Autoregressive Conditional Heteroscesdastic) models with skewed distributions using the Bayesian approach. Both parameter estimation and model comparison are not trivial tasks e approximating computationally intensive methods will be extensively used to this end. We propose a flexible class of multivariate distributions capable to simultaneously model skewness and kurtosis thus allowing for inference on these characteristics instead of fixing them a priori. (AU)

Articles published in Agência FAPESP Newsletter about the research grant:
Articles published in other media outlets (0 total):
More itemsLess items

Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
EHLERS, RICARDO; ZEVALLOS, M. Bayesian Estimation and Prediction of Stochastic Volatility Models via INLA. COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, v. 44, n. 3, p. 683-693, 2015. Web of Science Citations: 1.
FIORUCI, JOSE A.; EHLERS, RICARDO S.; ANDRADE FILHO, MARINHO G. Bayesian multivariate GARCH models with dynamic correlations and asymmetric error distributions. Journal of Applied Statistics, v. 41, n. 2, p. 320-331, FEB 1 2014. Web of Science Citations: 3.

Please report errors in scientific publications list by writing to: