Rogers-Satchell (RS) measure is an efficient volatility measure. This paper proposes quantile RS (QRS) measure to … on Standard and Poor 500 and Dow Jones Industrial Average indices show that volatility estimates using QRS measures …-of-sample forecast. For return models, the constant mean structure with Student-t errors and QRS volatility estimates provides the best …

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 …

strongest during bad economic times. In line with this evidence, we document that stock volatility predictability is also state …-series volatility models, in this paper we comprehensively examine how volatility forecastability varies across bull and bear states of … the stock market. We find that the volatility forecast horizon is substantially longer when the market is in a bear state …

There is evidence that volatility forecasting models that use intraday data provide better forecast accuracy as … fills this gap in the literature and extends previous studies on forecasting stock market volatility in several important …, we use forecast horizons ranging from 1 day to 6 months. Third, we evaluate the precision of volatility forecast provided …

volatility estimation is considered. The empirical analysis is performed on futures contracts of both the Standard and Poors 500 … importance of taking asymmetric effects (leverage effects) into account in volatility forecasts when it comes to risk management …

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 …

estimation of the state vector and of the time-varying parameters. We use this method to study the timevarying relationship …

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 …

for leverage effects in the realized volatility process and the long memory of the conditional variance of the HAR … component structure in asymmetric effects and a statistically significant long memory property in the “volatility of realized … volatility”. Compared with established HAR and ARFIMA realized volatility models, the proposed model exhibits superior in …

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 …