This paper proposes an up-to-date review of estimation strategies available for the Bayesian inference of GARCH-type models. The emphasis is put on a novel efficient procedure named AdMitIS. The methodology automatically constructs a mixture of Student-t distributions as an approximation to the...

This note presents the R package bayesGARCH (Ardia, 2007) which provides functions for the Bayesian estimation of the parsimonious and effective GARCH(1,1) model with Student-<I>t</I> innovations. The estimation procedure is fully automatic and thus avoids the tedious task of tuning a MCMC sampling...</i>

The increasing availability of financial market data at intraday frequencies has not only led to the development of improved ex-post volatility measurements but has also inspired research into their potential value as an informa-tion source for longer horizon volatility forecasts. In this paper...

We construct models which enable a decision-maker to analyze the implications of typical time series patterns of daily exchange rates for currency risk management. Our approach is Bayesian where extensive use is made of Markov chain Monte Carlo methods. The effects of several model...

Exchange rates typically exhibit time-varying patterns in both means and variances. The histograms of such series indicate heavy tails. In this paper we construct models which enable a decision-maker to analyze the implications of such time series patterns for currency risk management. Our...

Adaptive Polar Sampling (APS) is proposed as a Markov chain Monte Carlo method for Bayesian analysis of models with ill-behaved posterior distributions. In order to sample efficiently from such a distribution, a location-scale transformation and a transformation to polar coordinates are used....

It is generally believed that for the power of unit root tests, only the time span and not the observation frequency matters. In this paper we show that the observation frequency does matter when the high-frequency data display fat tails and volatility clustering, as is typically the case for...

In this paper we present an exact maximum likelihood treatment for the estimation of a Stochastic Volatility in Mean (SVM) model based on Monte Carlo simulation methods. The SVM model incorporates the unobserved volatility as an explanatory variable in the mean equation. The same extension is...

This paper considers a simple Continuous Beliefs System (CBS) to investigate the effects on price dynamics of several behavioral assumptions: (i) herd behaviour; (ii) a-synchronous updating of beliefs; and (iii) heterogeneity in time horizons (memory) among agents. The recently introduced...