. Applying our model to high-frequency transaction data, we detect two distinct regimes in the intraday volatility process: a … dominant volatility regime that is observable throughout the trading day representing the risk-transferring trading activity of … investors, and a minor volatility regime that concentrates around market liquidity shocks which mainly capture impacts of firm …

apply generalized method of moments (GMM) estimation. We find that we can get relatively accurate parameter estimates with … form the best linear forecasts for future volatility we find that the behavioral model generates sensible forecasts that …

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

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 …

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 …

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 …