Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/43278
Authors: 
Hautsch, Nikolaus
Hess, Dieter E.
Müller, Christoph
Year of Publication: 
2008
Series/Report no.: 
CFS Working Paper 2008/28
Abstract: 
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.
Subjects: 
Bayesian Learning
Macroeconomic Announcements
Information Quality
Precision Signals
JEL: 
E44
G14
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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