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 …

topological stock market changes as well as the incorporation of these topological changes into forecasting realized volatility … (RV) models to improve their forecast performance during turbulent periods. The results of the empirical experimentation … indicate that the employment of PH information allows nonlinear and neural network models to better forecast RV during a …

We estimate MIDAS regressions with various (bi)power variations to predict future volatility measured via increments in …

price volatility. To address this issue, we find a phenomenon, "momentum of jumps" (MoJ), that the predictive ability of the … jump component is persistent when forecasting the oil futures market volatility. Specifically, we propose a strategy that … according to their recent past forecasting performance. The volatility data are based on the intraday prices of West Texas …

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 …

forecast model that dominates all competitors. Focusing on Brazilian data, this paper aims to identify the existence of … models may vary over time. The problems of using individual models may be reduced by applying forecast combining schemes. The … empirical results show consistent forecast gains of combining schemes over time. In particular, the longer the forecast horizon …

proposed forecast and a benchmark. Considering stock return forecasting as an example, we show that the resulting robust … monitoring forecast improves the average performance of the proposed forecast by 15% (in terms of mean-squared-error) and reduces …