This paper extends the existing fully parametric Bayesian literature on stochastic volatility to allow for more general return distributions. Instead of specifying a particular distribution for the return innovation, we use nonparametric Bayesian methods to flexibly model the skewness and...

A definition of selfinformative Bayes carriers or limits is given as a description of an approach to noninformative Bayes estimation in non- and semiparametric models. It takes the posterior w.r.t. a prior as a new prior and repeats this procedure again and again. A main objective of the paper...

The papers in this special issue of Mathematics and Computers in Simulation are substantially revised versions of the papers that were presented at the 2011 Madrid International Conference on “Risk Modelling and Management” (RMM2011). The papers cover the following topics: currency hedging...

We investigate to what extent convergence in production levels per worker has been achieved in Germany since unification. To this end, we model the distribution of GDP per employee across German districts using two-component normal mixtures. While in the first year after unification, the two...

A common finding in empirical studies using micro data on consumer and producer prices is that hazard functions for price changes are decreasing. This means that a firm will have a lower probability of changing its price the longer it has kept it unchanged. This result is at odds with standard...

With the aim of constructing predictive distributions for daily returns, we introduce a new Markov normal mixture model in which the components are themselves normal mixtures. We derive the restrictions on the autocovariances and linear representation of integer powers of the time series in...

We derive restrictions for Granger noncausality in Markov-switching vector autoregressive models and also show under which conditions a variable does not affect the forecast of the hidden Markov process. Based on Bayesian approach to evaluating the hypotheses, the computational tools for...

Abstract Empirical research with Markov regime-switching models often requires the researcher not only to estimate the model but also to test for the presence of more than one regime. Despite the need for both estimation and testing, methods of estimation are better understood than are methods...

Abstract Within the context of a financial accelerator model, we model time-varying uncertainty (i.e. risk shocks) through the use of a mixture normal model with time variation in the weights applied to the underlying distributions characterizing entrepreneur productivity. Specifically, we model...

Abstract only: Today’s data analysts and modellers are in the luxurious position of being able to more closely describe, estimate, predict and infer about complex systems of interest, thanks to ever more powerful computational methods but also wider ranges of modelling distributions. Mixture...