Variational Bayesian Autoregressive Conditional Heteroskedastic Models
نویسنده
چکیده
A variational Bayesian autoregressive conditional heteroskedastic (VB-ARCH) model is presented. The ARCH class of models is one of the most popular for economic time series modeling. It assumes that the variance of the time series is an autoregressive process. The variational Bayesian approach results in an approximation to the full posterior distribution over ARCH model parameters, and provides a method for model selection. A novel application of Monte Carlo sampling is presented, wherein sampling is used to evaluate difficult terms in the variational free energy. A description of the variational approximation is followed by encouraging experimental results on model selection and volatility prediction on synthetic and historical financial data. Variational Bayesian Autoregressive Conditional Heteroskedastic Models Brian Sallans ÖFAI Neural Computation Group
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