parametric multivariate GARCH (MGARCH) benchmark models for returns, we consider an MGARCH with innovations following a Dirichlet … process mixture and an infinite hidden Markov model (IHMM). We introduce a new Bayesian semiparametric model that combines the … change in the unknown distribution of returns in portfolio allocation problems from a Bayesian perspective. Besides …

estimation method that takes these probabilistic inferences into account when relating state variables to observed data. In an …

correlation between trend and cycles. In this paper, I present a one step method, where DSGE structural parameters are jointly … estimates by Bayesian averaging. The role of investment shock as source of GDP volatility is reconsidered. …

Bayesian inference requires an analyst to set priors. Setting the right prior is crucial for precise forecasts. This … overall tightness parameter may increase to such an extent that Bayesian ADL becomes equivalent to frequentist ADL. …

true number of factors may still be large. For that reason I introduce in my analysis simple and efficient Bayesian model …

generic (linear and nonlinear) VARs. The performance of the proposed variable selection method is assessed in a small Monte …This paper develops methods for automatic selection of variables in forecasting Bayesian vector autoregressions (VARs … Carlo experiment, and in forecasting 4 macroeconomic series of the UK using time-varying parameters vector autoregressions …

period from 1961 to 2003. By employing two different methods to measure dependence in multivariate distributions – the …

on recovery rates is described through a theoretical Bayesian approach: possession probability conditional to Loan to …). The ratio between the two measures of loss, calculated at the 99.9th percentile for consistency with the regulatory …