, 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 …

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

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 …

and counterparty risks does not improve forecast accuracy and the predictability seems to derive from the econometric …

The volatility of equity and foreign exchange market is an important input to portfolio selection and to asset pricing … models. Many investment decisions and valuation of derivatives frequently rely on predictions of volatility. In this paper we … review the existing empirical literature in forecasting volatility of financial time series. Particularly, we decompose the …

realized volatility out-of-sample prediction performance relative to several extant forecast combinations. This result is …This paper investigates how to improve prediction accuracy of stock realized volatility using a large set of predictors … weight, we develop two simple and effective forecast combination methods. Using an array of 86 equity, bond, forex, futures …