a GARCH process for conditional volatility. Under such heteroskedasticity, OLS estimators or parameters in single …

It is sometimes argued that an increase in stock market volatility raises required stock returns, and thus lowers stock … for this volatility feedback effect. The resulting model is asymmetric, because volatility feedback amplifies large … for large crashes. The model also implies that volatility feedback is more important when volatility is high. In U …

-shifts of heterogeneous durations affect the volatility of dividend news. We estimate tightly parameterized specifications with … likelihood than the classic Campbell and Hentschel (1992) specification, while generating volatility feedback effects 6 to 12 … times larger. We show in an extension that Bayesian learning about stochastic volatility is faster for bad states than good …

Volatility permeates modern financial theories and decision making processes. As such, accurate measures and good … forecasts of future volatility are critical for the implementation and evaluation of asset pricing theories. In response to this …, a voluminous literature has emerged for modeling the temporal dependencies in financial market volatility at the daily …

We model the conditional mean and volatility of stock returns as a latent vector autoregressive (VAR) process to study … although the conditional correlation between the mean and volatility is negative, the unconditional correlation is positive due …

stock price reaction to news, and hence lowering the stock return volatility. Thus, in addition to uncertainty about … fundamentals, uncertainty about CEO quality is also a source of stock return volatility, which decreases over a CEO's tenure as the …'s prospects. Overall, uncertainty about management quality appears to be an important source of stock return volatility …

volatility over the next month, but with decreasing realized volatility. These predictability patterns are consistent with …