theory assumes that return shocks can be caused by changes in conditional volatility through a time-varying risk premium. On … leverage effect and the volatility feedback effect. We stress the importance of distinguishing between realized volatility and … implied volatility, and find that implied volatilities are essential for assessing the volatility feedback effect. The …

implications from a long-run risk model incorporating both time varying volatility and volatility uncertainty. We provide new … direct estimation of the underlying “structural” shocks and economic transmission mechanisms, including a new volatility …

A rapidly growing literature has documented important improvements in volatility measurement and forecasting … provides a practical framework for non-parametrically measuring the jump component in realized volatility measurements … an easy-to-implement reduced form model for realized volatility results in highly significant jump coefficient estimates …

frequency can be obtained almost as precisely as if volatility is observable by simply incorporating the strong information … content of realized volatility measures extracted from high-frequency data. For this purpose, we introduce asymptotically … exact volatility measurement equations in state space form and propose a Bayesian estimation approach. Our highly efficient …

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 …

controlling momentum, reversal, and volatility respectively. By using different combinations of parameter values, the process can …

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 …