of long-term stock market volatility. When long-term volatility is high, stock returns are more sensitive to news, and …-linear present value framework with a two-component volatility model for the conditional variance of cash flow news and allowing for … volatility feedback. In our model, innovations to the long-term volatility component are the most important driver of discount …

In this paper, we examine the relationship between idiosyncratic volatility and future returns around the firm … volatility is more strongly negative compared to news idiosyncratic volatility. Such findings imply that limited arbitrage cannot … fully explain the negative pricing of idiosyncratic volatility in the Chinese stock market. These results are robust after …

I propose a new model-free method for estimating long-run changes in expected volatility using VIX futures contracts …. The method is applied to measure the effect on stock market volatility of scheduled macroeconomic news announcements. I … when policy uncertainty is higher than average. Real side macro announcements increase long-run volatility during times of …

using inflation-induced equity market volatility (EMV) to better account for bond price determinants. The regression model …, a GED-GARCH (1,1) procedure, is adopted to deal with the volatility clustering and fat tail features in bond return … estimation. The testing results indicate that the inflation rate has a negative effect on bond returns across different …

volatility of Borsa Istanbul 100 Index (BIST-100). Sample data cover the period from January 2008 to December 2017. The main … nonlinear volatility models (symmetric and asymmetric Generalized AutoRegressive Conditional Heteroskedasticity [GARCH …]-type models) were used to model and estimate BIST-100 volatility in response to political news. The findings of the paper …