to approximate the conditional volatility, quantified with a GARCH(1,1) process, that is observed in empirical price data … investigate the two most prominent puzzles related to low-frequency stock prices: The conditional volatility of price returns, and …

volatility across di.erent time scales. We call this property asymmetric vertical dependence. It is asymmetric in the sense that … a low volatility state (regime) at a long time horizon is most likely followed by low volatility states at shorter time … horizons. On the other hand, a high volatility state at long time horizons does not necessarily imply a high volatility state …

Based on the APARCH model and two outlier detection methods, we computereliable time series of volatility asymmetry for … mostcountries. We nd that economic development and market capitalization/GDP arethe most important factors that increase volatility …

It is well known that the class of strong (Generalized) AutoRegressive Conditional Heteroskedasticity (or GARCH … individual parameters, the dynamics of the aggregate volatility involves additional lags that reflect the moments of the … to pay one fifth of her expected return to switch from the standard GARCH(1,1) estimator to the aggregation …

price volatility and “sentiment” fluctuations. We construct a general-equilibrium model of sentiment. In it, there are two …

discuss the relations between the results obtained and the phenomenon of ”volatility-induced growth” in stationary markets. …