variations in returns. Forecasting volatility has been a stimulating problem in the financial systems. This study examined the … forecasting technique with respect to various volatility estimators. The methodology of volatility estimation included Close …Volatility has been used as an indirect means for predicting risk accompanied with an asset. Volatility explains the …

variations in returns. Forecasting volatility had been a stimulating problem in the financial systems. The study examined the … forecasting technique with respect to various volatility estimators. The methodology of volatility estimation includes Close …Volatility had been used as an indirect means for predicting risk accompanied with the asset. Volatility explains 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 …

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 …

suggests that the introduction of asymmetric effects with respect to the returns and the volatility in the HAR model result in …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 …

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 …