A Bayesian non-parametric approach to asymmetric dynamic conditional correlation model with application to portfolio selection

Autor:	Virbickaite, Audrone, Ausín Olivera, María Concepción, Galeano San Miguel, Pedro
Přispěvatelé:	Universidad Carlos III de Madrid. Departamento de Estadística
Jazyk:	angličtina
Rok vydání:	2013
Předmět:	Bayesian non-parametrics Estadística Dirichlet process mixtures Asymmetric dynamic condition correlation Portfolio allocation
Zdroj:	e-Archivo. Repositorio Institucional de la Universidad Carlos III de Madrid instname
Popis:	We use an asymmetric dynamic conditional correlation (ADCC) GJR-GARCH model to estimate the time-varying volatilities of financial returns. The ADCC-GJR-GARCH model takes into consideration the asymmetries in individual assets volatilities, as well as in the correlations. The errors are modeled using a flexible location-scale mixture of infinite Gaussian distributions and the inference and estimation is carried out by relying on Bayesian non-parametrics. Finally, we carry out a simulation study to illustrate the flexibility of the new method and present a financial application using Apple and NASDAQ Industrial index data to solve a portfolio allocation problem The first and second authors are grateful for the financial support from MEC grant ECO2011-25706. The third author acknowledges financial support from MEC grant ECO2012-38442
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::ea995274d23583c221634a0d9c40e802 https://hdl.handle.net/10016/16967 Zobrazit plný text záznamu