Database. Incorporating this word embedding in a machine learning model produces a substantial increase in volatility … forecasting performance on days with volatility jumps for 23 NASDAQ stocks from 27 July 2007 to 18 November 2016. A simple … best forecasting performance for both normal and jump volatility days. Finally, we use Integrated Gradients and SHAP …

Many recent modelling advances in finance topics ranging from the pricing of volatility-based derivative products to … number of recent papers have addressed volatility predictability, some from the perspective of the usefulness of jumps in … forecasting volatility. Key papers in this area include Andersen, Bollerslev, Diebold and Labys (2003), Corsi (2004), Andersen …

We provide empirical evidence of volatility forecasting in relation to asymmetries present in the dynamics of both … return and volatility processes. Using recently-developed methodologies to detect jumps from high frequency price data, we … variation. The leverage effect is separated into continuous and discontinuous effects, and past volatility is separated into …

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 …

In this paper, we estimate, model and forecast Realized Range Volatility, a new realized measure and estimator of the …-known stylized effects present in financial data. We consider an HAR model with asymmetric effects with respect to the volatility and …

In this paper, we estimate, model and forecast Realized Range Volatility, a realized measure and estimator of the … forecasting daily stocks volatility. We consider an HAR model with asymmetric effects with respect to the volatility and the … volatility series and they have a highly in-sample explanatory power. The analysis of the forecast performance in 16 NYSE stocks …

volatility (RV) measures. The absolute difference between daily and monthly RV is shown to be proportional to the relative …

This paper introduces a parsimonious and yet flexible semiparametric model to forecast financial volatility. The new …

This paper proposes a novel decomposition of realized volatility (RV) into moderate and extreme realized volatility … estimates. These estimates behave like long and short term components of volatility, and are very different from either realized … semi-variance or the continuous and jump components of volatility. Within the standard linear HAR framework, a forecast …