Missing Events in Event Studies: Identifying the Effects of Partially-Measured News Surprises
63 Pages Posted: 17 Sep 2018 Last revised: 4 Oct 2019
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Missing Events in Event Studies: Identifying the Effects of Partially-Measured News Surprises
Missing Events in Event Studies: Identifying the Effects of Partially-Measured News Surprises
Date Written: September 2018
Abstract
Macroeconomic news announcements are elaborate and multi-dimensional. We consider a framework in which jumps in asset prices around macroeconomic news and monetary policy announcements reflect both the response to observed surprises in headline numbers and latent factors, reflecting other details of the release. The details of the non-headline news, for which there are no expectations surveys, are unobservable to the econometrician, but nonetheless elicit a market response. We estimate the model by the Kalman filter, which essentially combines OLS- and heteroskedasticity-based event study estimators in one step, showing that those methods are better thought of as complements rather than substitutes. The inclusion of a single latent factor greatly improves our ability to explain asset price movements around announcements.
Keywords: Bond Markets, event study, high-frequency data, identification
JEL Classification: E43, E52, E58, G12, G14
Suggested Citation: Suggested Citation
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Missing Events in Event Studies: Identifying the Effects of Partially-Measured News Surprises
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