I. The Statistical Model -- 1.1 Notation -- 1.2 Interpretation -- 1.3 Likelihood function -- II. Bayesian Inference: The Extended Natural-Conjugate Approach -- II.1 Two reformulations of the likelihood function -- II.2 The extended natural-conjugate prior density -- II.3 Posterior densities --...

GARCH volatility models with fixed parameters are too restrictive for long time series due to breaks in the volatility process. Flexible alternatives are Markov-switching GARCH and change-point GARCH models. They require estimation by MCMC methods due to the path dependence problem. An unsolved...

A new model - the factorial hidden Markov volatility (FHMV) model - is proposed for financial returns and their latent variances. It is also applicable to model directly realized variances. Volatility is modeled as a product of three components: a Markov chain driving volatility persistence, an...

Realized covariance models specify the conditional expectation of a realized covariance matrix as a function of past realized covariance matrices through a GARCH-type structure. We compare the forecasting performance of several such models in terms of economic value, measured through economic...

We model the dynamic volatility and correlation structure of electricity futures of the European Energy Exchange index. We use a new multiplicative dynamic conditional correlation (mDCC) model to separate long-run from short-run components. We allow for smooth changes in the unconditional...