Publication: A Bayesian non-parametric approach to asymmetric dynamic conditional correlation model with application to portfolio selection
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2013-05
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Abstract
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
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Keywords
Asymmetric dynamic condition correlation, Bayesian non-parametrics, Dirichlet process mixtures, Portfolio allocation