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

frequency volatilities and correlations ; Dynamic conditional correlation ; Spline-GARCH ; Idiosyncratic volatility ; Long …

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 …

-switching models, and forecast combination to predict the dynamics in the S&P 500. First, we aggregate the weekly information of 115 …

respective forecast accuracy and test whether the forecasted information are relevant to equity or debt markets. The results, in … terms of forecast accuracy in firm-level tests. This study finds that the opposing performance outcome appears to be driven …

bad) stock market volatility, we show that incorporating the information in lagged industry returns can help improve out …-of sample forecasts of aggregate stock market volatility. While the predictive contribution of industry level returns is not … crisis, highlighting the informational value of real economic activity on stock market volatility dynamics. Finally, we show …

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

Recent contributions highlight the importance of intraday jumps in forecasting realized volatility at horizons up to … importance of considering the continuous/jump decomposition of volatility for the purpose of density forecasting. Specifically …

predictability, popular predictors from the literature fail to outperform the simple historical average benchmark forecast in out … model restrictions, forecast combination, diffusion indices, and regime shifts—improve forecasting performance by addressing …