Forecasting-volatility models typically rely on either daily or high frequency (HF) data and the choice between these … two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer of many … forecasting-volatility models, comparing their performance (in terms of Value at Risk, VaR) under the assumptions of jumping …

Forecasting volatility models typically rely on either daily or high frequency (HF) data and the choice between these … two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer from … these two family forecasting-volatility models, comparing their performance (in terms of Value at Risk, VaR) under the …

Both unconditional mixed-normal distributions and GARCH models with fat-tailed conditional distributions have been … employed for modeling financial return data. We consider a mixed-normal distribution coupled with a GARCH-type structure which … can generate a plausible disaggregation of the conditional variance process, in which the components' volatility dynamics …

Volatility (SV) and Generalised Autoregressive Conditional Heteroskedasticity (GARCH) models which are both extended to include … improved ex-post volatility measurements but has also inspired research into their potential value as an informa-tion source … for longer horizon volatility forecasts. In this paper we explore the forecasting value of these high fre-quency series in …