We revisit the relation between stock market volatility and macroeconomic activity using a new class of component models that distinguish short run from secular movements. We combine insights from Engle and Rangel (2007) and the recent work on mixed data sampling (MIDAS), as in e.g. Ghysels,...

We revisit the relation between stock market volatility and macroeconomic activity using a new class of component models that distinguish short run from secular movements. We combine insights from Engle and Rangel (2007) and the recent work on mixed data sampling (MIDAS), as in e.g. Ghysels,...

We propose a new class of dynamic order book models that allow us to 1) study episodes of extreme low liquidity and 2) unite liquidity and volatility in one framework through which their joint dynamics can be examined. Liquidity and volatility in the U.S. Treasury securities market are analyzed...

We model the joint dynamics of intraday liquidity, volume, and volatility in the U.S. Treasury market, especially through the 2007--09 financial crisis and around important economic announcements. Using various specifications based on Bauwens & Giot (2000)'s Log-ACD(1,1) model, we find that...

The idea of component models for volatility is extended to dynamic correlations. We propose a model of dynamic correlations with a short- and long-run component specification. We call this class of models DCC-MIDAS as the key ingredients are a combination of the Engle (2002) DCC model, the Engle...

We discuss the nature of risk valid factors should represent. The Campbell's (1993) ICAPM extended with heteroskedastic asset returns guides us to identify the risk; we show that many of empirically well-established factors contain information about the future changes in the investment...

We study regression models that involve data sampled at different frequencies. We derive the asymptotic properties of the NLS estimators of such regression models and compare them with the LS estimators of a traditional model that involves aggregating or equally weighting data to estimate a...