This paper proposes a Bayesian nonparametric modeling approach for the return distribution in multivariate GARCH models … methods, which provide mixing over both the location and scale of the normal components. MCMC methods are introduced for …

; cumulative Bayes factor ; Dirichlet process mixture ; forecasting ; infinite mixture model ; MCMC ; slice sampler …This paper proposes a Bayesian nonparametric modeling approach for the return distribution in multivariate GARCH models … methods, which provide mixing over both the location and scale of the normal components. MCMC methods are introduced for …

This paper proposes a Bayesian nonparametric modeling approach for the return distribution in multivariate GARCH models … methods, which provide mixing over both the location and scale of the normal components. MCMC methods are introduced for …

exact volatility measurement equations in state space form and propose a Bayesian estimation approach. Our highly efficient … estimates lead in turn to substantial gains for forecasting various risk measures at horizons ranging from a few days to a few …

regime are both allowed. A Bayesian learning approach is employed to jointly estimate the latent states and the model … parameters in real time. An important feature of our Bayesian method is that we are able to deal with parameter uncertainty and …

This paper extends the existing fully parametric Bayesian literature on stochastic volatility to allow for more general … Bayesian methods to flexibly model the skewness and kurtosis of the distribution while continuing to model the dynamics of … volatility with a parametric structure. Our semiparametric Bayesian approach provides a full characterization of parametric and …

posteriors. We develop a Bayesian Markov chain Monte Carlo sampler to fully characterize the parametric and distributional …

posteriors. We develop a Bayesian Markov chain Monte Carlo sampler to fully characterize the parametric and distributional … forecasting more accurate empirical market returns. A major reason is how volatility responds to an unexpected market movement …

posteriors. We develop a Bayesian Markov chain Monte Carlo sampler to fully characterize the parametric and distributional … forecasting more accurate empirical market returns. A major reason is how volatility responds to an unexpected market movement …