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Multivariate GARCH models with skewed distributions

Grant number: 11/22317-0
Support type:Regular Research Grants
Duration: March 01, 2012 - February 28, 2014
Field of knowledge:Physical Sciences and Mathematics - Probability and Statistics
Principal Investigator:Ricardo Sandes Ehlers
Grantee:Ricardo Sandes Ehlers
Home Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil


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)

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.

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