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

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

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 …

In this paper, we estimate, model and forecast Realized Range Volatility, a new realized measure and estimator of the …-known stylized effects present in financial data. We consider an HAR model with asymmetric effects with respect to the volatility and … distribution for the innovations. The analysis of the forecast performance during the different periods suggests that including the …

volatility (RV) of ten global stock market indices in the period from January 2000 to December 2021. We train models using a …

We use learning in an equilibrium model to explain the puzzling predictive power of the volatility risk premium (VRP …

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 …

We investigate the question of whether macroeconomic variables contain information about future stock volatility beyond … that contained in past volatility. We show that forecasts of GDP growth from the Federal Reserve's Survey of Professional … Forecasters predict volatility in a cross-section of 49 industry portfolios. The expectation of higher growth rates is associated …