intra-daily models, such as the Realized Volatility and inter-daily models, such as the ARCH class. The forecasting …A new variant of the ARCH class of models for forecasting the conditional variance, to be called the Generalized … the extreme values, is a good alternative to the Realized Volatility that requires a large amount of intra-daily data …

estimation of the volatility in the market plays a key role in quantifying market risk exposure correctly. This paper presents … GARCH models which capture volatility clustering and, therefore, are appropriate to analyse financial market data. Models … time-varying volatility. In this paper, the estimation of conditional volatility is applied to Value at Risk measurement …

estimation of the volatility in the market plays a key role in quantifying market risk exposure correctly. This paper presents … GARCH models which capture volatility clustering and, therefore, are appropriate to analyse financial market data. Models … time-varying volatility. In this paper, the estimation of conditional volatility is applied to Value at Risk measurement …

estimation of the volatility in the market plays a key role in quantifying market risk exposure correctly. This paper presents … GARCH models which capture volatility clustering and, therefore, are appropriate to analyse financial market data. Models … time-varying volatility. In this paper, the estimation of conditional volatility is applied to Value at Risk measurement …

A Hidden Markov Model (HMM) is used to model the VIX (the Cboe Volatility Index). A 4- state Gaussian mixture is fitted … Hedge Index). The results presented here show promising application in modelling and predicting volatility, as well as … identifying current volatility regimes predominating the market …

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 …

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 …

exhibits clustering volatility. This gap leads to introduce a competing model to catch up with the clustering volatility and …

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 …

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 …