Price adjustment to news with uncertain precision

Bayesian learning provides the core concept of processing noisy information. In standard Bayesian frameworks, assessing the price impact of information requires perfect knowledge of news’ precision. In practice, however,
Bayesian learning provides the core concept of processing noisy information. In standard Bayesian frameworks, assessing the price impact of information requires perfect knowledge of news’ precision. In practice, however, precision is rarely dis- closed. Therefore, we extend standard Bayesian learning, suggesting traders infer news’ precision from magnitudes of surprises and from external sources. We show that interactions of the different precision signals may result in highly nonlinear price responses. Empirical tests based on intra-day T-bond futures price reactions to employment releases confirm the model’s predictions and show that the effects are statistically and economically significant.
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Metadaten
Author:Nikolaus Hautsch, Dieter Hess, Christoph Müller
URN:urn:nbn:de:hebis:30-57665
Parent Title (German):CFS working paper series ; 2008, 28
Series (Serial Number):CFS working paper series (2008, 28)
Document Type:Working Paper
Language:English
Date of Publication (online):2008/09/24
Year of first Publication:2008
Publishing Institution:Univ.-Bibliothek Frankfurt am Main
Release Date:2008/09/24
HeBIS PPN:205704204
Institutes:Center for Financial Studies (CFS)
Dewey Decimal Classification:330 Wirtschaft
JEL-Classification:E44 Financial Markets and the Macroeconomy
G14 Information and Market Efficiency; Event Studies
Sammlungen:Universitätspublikationen
Licence (German):License Logo Veröffentlichungsvertrag für Publikationen

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