This paper introduces a parsimonious and yet flexible semiparametric model to forecast financial volatility. The new …

The GARCH(1,1) model and its extensions have become a standard econometric tool for modeling volatility dynamics of … on the basis of their one-step ahead forecasting performance. With regard to forecast unbiasedness and precision …

The GARCH(1,1) model and its extensions have become a standard econometric tool for modeling volatility dynamics of … on the basis of their one-step ahead forecasting performance. With regard to forecast unbiasedness and precision …

more than a single regime, have performed substantially better than standard methods in terms of volatility and Value … individual models, we evaluate the use of forecast combinations strategies. In our empirical application, procedures that are …

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 …

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

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 …