for conditional heteroskedasticity; a favored model is Dynamic Conditional Correlation (DCC), derived from the ARCH/GARCH …

investigate the sources of heavy tails by estimating autoregressive models in which innovations can be subject to GARCH effects … assessed both within- and out-of-sample. We find strong evidence in favour of modelling both GARCH effects and non …

Conditional heteroskedasticity of the error terms is a common occurrence in financial factor models, such as the CAPM …

Current models for predicting volatility do not incorporate information flow and are solely based on historical … predictor of its stock volatility. The results show that future stock volatility is better predicted by our method than the … flow or as an active source for new information influencing future volatility. Our data suggest that semantic content may …

the stock market volatility is compared in both the Anglophone world and the Sinophone world. I find that the stock market … volatility and the number of publicly available global news stories are strongly linked to each other in both languages …. Contemporaneous correlations between news and volatility are positive and highly significant, and regressions tell us that the …

This paper addresses stock market volatility in Germany between 1991 and 2018. Through a GARCH model with leverage term …, an estimation of volatility in the DAX is provided. Such estimation is then plugged into a quantile regression model … where potential economic determinants are analyzed. The results suggest that stock market volatility in Germany reached its …