A Bayesian non-parametric approach to asymmetric dynamic conditional correlation model with application to portfolio selection
AbstractWe 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
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Bibliographic InfoPaper provided by Universidad Carlos III, Departamento de Estadística y Econometría in its series Statistics and Econometrics Working Papers with number ws131009.
Date of creation: May 2013
Date of revision:
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Asymmetric dynamic condition correlation; Bayesian non-parametrics; Dirichlet process mixtures; Portfolio allocation;
Other versions of this item:
- Audrone Virbickaite & M. Concepci\'on Aus\'in & Pedro Galeano, 2013. "A Bayesian Non-Parametric Approach to Asymmetric Dynamic Conditional Correlation Model With Application to Portfolio Selection," Papers 1301.5129, arXiv.org, revised Jan 2014.
- NEP-ALL-2013-05-24 (All new papers)
- NEP-ECM-2013-05-24 (Econometrics)
- NEP-ETS-2013-05-24 (Econometric Time Series)
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