popular linear and nonlinear GARCH-type models is used to investigate this relevancy. Our in-sample and out-of-sample results …

using the following ARFIMA–GARCH-class models: ARIMA–GARCH, ARFIMA–GARCH, ARFIMA–IGARCH, and ARFIMA–FIGARCH. Although the …

forecasting the conditional volatility of oil spot and futures prices using a variety of GARCH-type models. Our results can be … summarized as follows. First, we provide evidence of parameter instability in five out of nine GARCH-based conditional volatility …

the high-frequency EUA price dynamics are very well captured by a fractionally integrated asymmetric power GARCH process …

production behavior in response to its “cut”, “maintain”, and “increase” decisions. Then by applying the ARMA–GARCH class models …

This article examines the volatility forecasting abilities of three approaches: GARCH-type model that uses carbon … document that GARCH-type models perform better than an implied volatility and the k-nearest neighbor model. This result …

moving average (ARMA) models with generalized autoregressive conditional heteroskedasticity (GARCH) processes, namely ARMA–GARCH … models, along with their modified forms, ARMA–GARCH-in-mean (ARMA–GARCH-M), to model and forecast hourly ahead electricity … the ARMA–GARCH based time series forecasting of electricity prices. Multiple statistical measures are employed to evaluate …

, this paper estimates and evaluates the forecasting performance of four ARMAX–GARCH models for five MISO pricing hubs … by a simple GARCH model and thus are less complex. …