logistic auto-regressive processes, change over time and their dynamics are possible driven by the past forecasting … performances of the predictive densities. For illustrative purposes we apply it to combine White Noise and GARCH models to forecast …

logistic autoregressive processes, change over time and their dynamics are possible driven by the past forecasting performances … of the predictive densities. For illustrative purposes we apply it to combine White Noise and GARCH models to forecast …

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 …

forecasting volatility model with the most appropriate error distribution. The results suggest the presence of leverage effect … validate this result. The last twenty eight days out-of-sample forecast adjudged Power-GARCH (1, 1, 1) in student's t error … forecasting model that could guarantee a sound policy decisions. …

estimates lead in turn to substantial gains for forecasting various risk measures at horizons ranging from a few days to a few …

, both in simulation and when forecasting a large cross section of industry portfolios spanning almost a hundred years of …

Using well-known GARCH models for density prediction of daily S&P 500 and Nikkei 225 index returns, a comparison is …

conditional heteroskedastic (GARCH) with the innovation following (1) mixture of generalized Pareto and Gaussian distributions and … where the results from the in-sample period before the COVID-19 pandemic justify the use of the proposed GARCH models. The …