This paper describes a GAUSS program of a Markov-chain sampling algorithm for GARCH models proposed by Nakatsuma (1998 …). This algorithm allows us to generate Monte Carlo samples of parameters in a GARCH model from their joint posterior … distribution. The samples obtained by this algorithm are used for Bayesian analysis of the GARCH model. As numerical examples …

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

We develop univariate regime-switching GARCH (RS-GARCH) models wherein the conditional variance switches in time from … one GARCH process to another. The switching is governed by a time-varying probability, specified as a function of past …

This paper explains how the Gibbs sampler can be used to perform Bayesian inference on GARCH models. Although the Gibbs … available in regression models with GARCH errors. We show that the Gibbs sampler can be combined with a unidimensional … compared with importance sampling and the Metropolis-Hastings algorithm. It is applied to estimate an asymmetric Student-GARCH …

We consider the estimation of a large number of GARCH models, of the order of several hundreds. Our interest lies in …

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 …

This chapter proposes an up-to-date review of estimation strategies available for the Bayesian inference of GARCH … empirical application to S&P index log-returns where non-nested GARCH-type models are estimated and combined to predict the …

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 …