forecasting the volatility of equity prices, using high-frequency data from 2000 to 2016. We consider the SPY and 20 stocks that …, 60, and 300 seconds), forecast horizons (1, 5, 22, and 66 days) and the use of standard and robust-to-noise volatility … real-time forecasts than the HAR-RV model, although no single extended model dominates. In general, standard volatility …

the financial crisis, suggesting that an extreme volatility period requires models that can adapt quickly to turmoil …

Volatility has been used as an indirect means for predicting risk accompanied with an asset. Volatility explains the … variations in returns. Forecasting volatility has been a stimulating problem in the financial systems. This study examined the … different volatility estimators and determined the most efficient volatility estimator. The study described the accuracy of the …

Volatility had been used as an indirect means for predicting risk accompanied with the asset. Volatility explains the … variations in returns. Forecasting volatility had been a stimulating problem in the financial systems. The study examined the … different volatility estimators and determined the efficient volatility estimator. The study described the accuracy of …

There is evidence that volatility forecasting models that use intraday data provide better forecast accuracy as … fills this gap in the literature and extends previous studies on forecasting stock market volatility in several important …, we use forecast horizons ranging from 1 day to 6 months. Third, we evaluate the precision of volatility forecast provided …

We propose a new class of conditional heteroskedasticity in the volatility (CH-V) models which allows for time …-varying volatility of volatility in the volatility of asset returns. This class nests a variety of GARCH-type models and the SHARV model … of Ding (2021b). CH-V models can be seen as a special case of the stochastic volatility of volatility model. We then …

A Monte Carlo (MC) experiment is conducted to study the forecasting performance of a variety of volatility models under … Autoregressive Conditional Heteroskedasticity models ((FI)GARCH), the Stochastic Volatility model (SV) and the Markov … different characterizations of the latent volatility process: specifications which incorporate short/long memory, autoregressive …