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 …

-state Markov-Switching GARCH models were used in the study to determine the best model for modelling and forecasting the stock … Criteria, E-GARCH variance specification with skewed student- t innovation was shown to be appropriate for modelling the stock …

practitioners. The GARCH model has been exceptionally successful in this area. Our approach, the minimally cross …-entropic conditional density (MCECD) model, is a generalization of GARCH(1,1) which can cope with conditional skewness and kurtosis. It is …

conditional heteroskedasticity (GARCH) model is used to identify the magnitude and significance of mean and volatility spillovers … of strong ARCH and GARCH effects. Contrary to evidence from studies in North American electricity markets, the results …

This study employs an extended version of the Generalised Autoregressive Conditional Heteroskedasticity in Mean (GARCH …

on Giamouridis and Vrontos (2007), a broad set of multivariate GARCH models, as well as, the simpler exponentially … weighted moving average (EWMA) estimator of RiskMetrics (1996) are considered. It is found that, while multivariate GARCH … of range, regime switching, nonlinear filtration, and GARCH frameworks. Any incremental improvement in the performance of …

price behavior of CZCE with that of the Chicago Board of trade (CBOT) in the US using ARCH/GARCH based univariate and …). Results show both markets can be modeled by an ARCH (1) or a GARCH (1,1), and the models have better fit when conditional …

This paper examines the supply response of the Greek pork market. A GARCH process isused to estimate expected price and … price volatility, while price and supply equations areestimated jointly. In addition to the standard GARCH model, several … different symmetric,asymmetric, and nonlinear GARCH models are estimated. The empirical results indicate thatamong the estimated …

sectoral-level, monthly data and an innovative multivariate GARCH-M estimator with corrections for leptokurtic errors. This …