for longer horizon volatility forecasts. In this paper we explore the forecasting value of these high fre-quency series in … Volatility (SV) and Generalised Autoregressive Conditional Heteroskedasticity (GARCH) models which are both extended to include … the intraday volatility measure. For forecasting horizons ranging from one day to one week the most accurate out …

This book presents in detail methodologies for the Bayesian estimation of single-regime and regime-switching GARCH … the Bayesian paradigm for inference. The next three chapters describe the estimation of the GARCH model with Normal … applications of the Bayesian estimation of GARCH models. We show how agents facing different risk perspectives can select their …

A sequential Monte Carlo method for estimating GARCH models subject to an unknown number of structural breaks is … breaks; while more flexible return distributions such as t-innovations or a GARCH-jump mixture model still favors breaks but …

A sequential Monte Carlo method for estimating GARCH models subject to an unknown number of structural breaks is … breaks; while more flexible return distributions such as t-innovations or a GARCH-jump mixture model still favors breaks but …

) models affects their forecasting performance. We apply this analysis to medium-scale DSGE models with and without financial … with financial frictions outperform in forecasting inflation but not the GDP growth rate. …

-asset volatility, variances & correlations, GARCH, MLE.Modeled VaR, QMLE, non-normality, Cornish-Fisher, EVT stochastic models for …