A full Bayesian analysis of GARCH and EGARCH models is proposed consisting of parameter estimation, model selection and volatility prediction. The Bayesian paradigm is implemented via Markov-chain Monte Carlo methodologies. We provide implementation details and illustrations using the General...

A new multivariate time series model with time varying conditional variances and covariances is presented and analysed. A complete analysis of the proposed model is presented consisting of parameter estimation, model selection and volatility prediction. Classical and Bayesian techniques are used...

In this paper we suggest a Bayesian approach for inferring stationary autoregressive models allowing for possible structural changes (known as breaks) in both the mean and the error variance of economic series occuring at unknown times. Efficient Bayesian inference for the unknown number and...

In this paper we develop a framework for asset-liability management for pension funds in a time-varying volatility environment. We use sophisticated dynamic econometric models for the variances-covariances of the asset classes in which the pension fund is investing, while keeping the liability...

This paper extends the complete subset linear regression framework to a quantile regression setting. We employ complete subset combinations of quantile forecasts in order to construct robust and accurate equity premium predictions. Our recursive algorithm that selects, in real time, the best...