Bayesian Inference for the Mixed Conditional Heteroskedasticity Model
Copyright policy )Bayesian Inference for the Mixed Conditional Heteroskedasticity Model
Summary: We estimate by Bayesian inference the mixed conditional heteroskedasticity model of \textit{M. Haas} et al. [Mixed normal conditional heteroskedasticity. J. Financial Econ. 2, 211--250 (2004)]. We construct a Gibbs sampler algorithm to compute posterior and predictive densities. The number of mixture components is selected by the marginal likelihood criterion. We apply the model to the SP500 daily returns.
Applications of statistics to actuarial sciences and financial mathematics, Finite mixture, finite mixture, ML estimation, Bayesian inference, Value at Risk, Bayesian inference, marginal likelihood, Finite mixure; ML estimation; Bayesian inference; Value at Risk, value at risk, ML estimation, Value at Risk, finite mixtures, Finite mixture, ML estimation, bayesian inference, value at risk., jel: jel:C32, jel: jel:C11, jel: jel:C15
Applications of statistics to actuarial sciences and financial mathematics, Finite mixture, finite mixture, ML estimation, Bayesian inference, Value at Risk, Bayesian inference, marginal likelihood, Finite mixure; ML estimation; Bayesian inference; Value at Risk, value at risk, ML estimation, Value at Risk, finite mixtures, Finite mixture, ML estimation, bayesian inference, value at risk., jel: jel:C32, jel: jel:C11, jel: jel:C15
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