Much recent research in finance has found empirical evidence of jumps in equity returns. Their presence has been successfully used to better explain various market phenomena. Nevertheless, the role of real-time information for predicting jumps in stock markets has not been thoroughly investigated in the literature. In this article, I analyze the predictability of jumps in individual stock returns, using both macroeconomic and firm-specific news releases and I present how the information is reflected in stock prices as jumps. This analysis naturally allows a novel decomposition of individual stock jumps into systematic and idiosyncratic jumps.
There’s a agreement that there exists a strong link between too much regulation, called “market frictions”, and economic under-performance. Indeed, a growing body of literature claims market frictions are to blame for the divergent performance in productivity and employment of continental European versus US economies during the 80s and 90s. But, if European markets should be deregulated, why doesn’t it happen? While product market reforms are slow-moving in Europe and some sectors are still virtually served by monopolies, labour market deregulation is even less pronounced (Gönenç et al., 2000).
There is an active discussion in the literature on whether the standard search and matching model (Mortensen and Pissarides (1994), Pissarides (1985, 2000)) is consistent with U.S. business cycle facts. The issue that received the most attention is whether the model can generate the volatility of labor market variables (e.g., vacancies, unemployment) that is quantitatively consistent with the data. Shimer (2005) proposed a calibration of the model that implied that the model generates only a small volatility in the variables of interest. Hagedorn and Manovskii (2006), on the other hand, propose a different calibration strategy, and find that the model does generate a volatile labor market.
