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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Date of creation: May 2013
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Asymmetric dynamic condition correlation; Bayesian non-parametrics; Dirichlet process mixtures; Portfolio allocation;
This paper has been announced in the following NEP Reports:
- 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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