Multivariate GARCH models with skewed distributions

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

Abstract

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)

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. (11/22317-0)

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. (11/22317-0)