. GARCH and DCC models with changing parameters are specified using the sticky infinite hidden Markov-chain framework …

GARCH volatility models with fixed parameters are too restrictive for long time series due to breaks in the volatility … process. Flexible alternatives are Markov-switching GARCH and change-point GARCH models. They require estimation by MCMC …

We develop a Markov-switching GARCH model (MS-GARCH) wherein the conditional mean and variance switch in time from one … GARCH process to another. The switching is governed by a hidden Markov chain. We provide sufficient conditions for geometric …

metric. The methods are illustrated using an asymmetric GARCH model with a data set on a stock index in Brussels. The …

The GARCH and stochastic volatility (SV) models are two competing, well-known and often used models to explain the … simple, strongly consistent decision rules to compare the ability of the GARCH and the SV model to fit the characteristic …

We present a novel GARCH model that accounts for time varying, state dependent, persistence in the volatility dynamics …. The proposed model generalizes the component GARCH model of Ding and Granger (1996). The volatility is modelled as a … convex combination of unobserved GARCH components where the combination weights are time varying as a function of …

We consider the estimation of a large number of GARCH models, of the order of several hundreds. To achieve parsimony …

series, namely ARIMA-GARCH models. We present a unified overview of temporal aggregation techniques for this broad class of …

parsimony and robustness. APS is applied within a Bayesian analysis of a GARCH-mixture model which is used for the evaluation of …