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 …

The contributions of error distributions have been ignored while modeling stock market volatility in Nigeria and … studies have shown that the application of appropriate error distribution in volatility model enhances efficiency of the model … asymmetric volatility models each in Normal, Student's-t and generalized error distributions with the view to selecting the best …

the new model's moment properties are also derived. Empiricalresults are given for the daily returns of the compositeindex …

This paper documents law of one price violations in equity volatility markets. While tightly linked by no … stress and predict the returns of VIX futures. A relative value trading strategy based on the deviation measure earns a large …

In this paper, we apply machine learning to forecast the conditional variance of long-term stock returns measured in … of covariates as well as the smoothing parameters via cross-validation. We find that volatility forecastability is much … squared return prediction errors gives an adequate approximation of the unobserved realised conditional variance for both the …

In this paper, we use factor-augmented HAR-type models to predict the daily integrated volatility of asset returns. Our … approach is based on a proposed two-step dimension reduction procedure designed to extract latent common volatility factors … from a large dimensional and high-frequency returns dataset with 267 constituents of the S&P 500 index. In the first step …

We use machine learning methods to predict stock return volatility. Our out-of-sample prediction of realised volatility … realised volatility of 43.8% with an R2 being as high as double the ones reported in the literature. We further show that … machine learning methods can capture the stylized facts about volatility without relying on any assumption about the …

The persistent nature of equity volatility is investigated by means of a multi-factor stochastic volatility model with … model a time-varying generalization of the HAR model for the realized volatility series. It emerges that during the recent … stochastic volatility model suggest that the change in the dynamic structure of the realized volatility during the financial …