This study reconsiders the role of jumps for volatility forecasting by showing that jumps have a positive and mostly significant impact on future volatility. This result becomes apparent once volatility is separated into its continuous and discontinuous component using estimators which are not...

This paper investigates the link between Bitcoin and macroeconomic fundamentals by estimating the impact of macroeconomic news on Bitcoin using an event study with intraday data. The key result is that, unlike other U.S. asset classes, Bitcoin is orthogonal to monetary and macroeconomic news....

We show a significant loss in U.S. Treasury market functionality when intensive use of dealer balance sheets is needed to intermediate bond markets, as in March 2020. Although yield volatility explains most of the variation in Treasury market liquidity over time, when dealer balance sheet...

We show that realized volatility, especially the realized volatility of financial sector stock returns, has strong predictive content for the future distribution of market returns. This is a robust feature of the last century of U.S. data and, most importantly, can be exploited in real time....

We propose a new method for multivariate forecasting which combines the Generalized Dynamic Factor Model (GDFM) and the multivariate Generalized Autoregressive Conditionally Heteroskedastic (GARCH) model. We assume that the dynamic common factors are conditionally heteroskedastic. The GDFM,...

We estimate a DSGE model where rare large shocks can occur, but replace the commonly used Gaussian assumption with a Student's t-distribution. Results from the Smets and Wouters (2007) model estimated on the usual set of macroeconomic time series over the 1964-2011 period indicate that 1) the...

The possibility to measure the relative contribution of agents and exchanges to the price formation process in high-frequency financial markets acquired increasingly importance in the financial econometric literature. In this paper I propose to adopt fully data-driven approaches to identify...

I propose to identify announcement-specific decompositions of asset price changes into monetary policy shocks based on intraday time-varying volatility. This approach is the first to accommodate changes in both the nature of shocks and the state of the economy across announcements. I compute...

An n-variable structural vector auto-regression (SVAR) can be identified (up to shock order) from the evolution of the residual covariance across time if the structural shocks exhibit heteroskedasticity (Rigobon (2003), Sentana and Fiorentini (2001)). However, the path of residual covariances is...

I propose a new measure of price discovery, which I will refer to as the Independent Component based Information Share (IC-IS). This measure constitutes a variant of the widespread Information Share, with the main difference being it does not suffer the same identification issues. Under the...