three US financial assets, we compare the realized MEGARCH models with existing multivariate GARCH class models. The …The paper develops a new realized matrix-exponential GARCH (MEGARCH) model, which uses the information of returns and … realized measure of co-volatility matrix simultaneously. The paper also considers an alternative multivariate asymmetric …

. The model is estimated with sequential Monte Carlo methods that include a particle learning filter and a Rao …

This paper develops Bayesian econometric methods for posterior inference in non-parametric mixed frequency VARs using …

approach using a Bayesian MS-VAR which is net of these arbitrary components. This method allows for the consistent … method on artificial as well a real data and conduct an empirical backtest, in which generated scenarios are compared to the … actual development during the financial crisis. The method is challenged with the DSGE model and conditional forecasting. …

This paper develops Bayesian econometric methods for posterior inference in non-parametric mixed frequency VARs using …

We develop a dynamic Bayesian model for clickthrough and conversion probabilities of paid search advertisements. These …

We consider the estimation of a large number of GARCH models, of the order of several hundreds. To achieve parsimony … distribution has its own conditional mean and variance. Inference is done by the Bayesian approach, using data augmentation …

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-t innovations. The estimation procedure is fully automatic and thus avoids …

exuberance. To shed some light on the magnitude of the deviation of stock prices from fundamentals we apply a Markov …