Price Adjustment to News with Uncertain Precision

Nikolaus Hautsch (), Dieter Hess () and Christoph Müller ()
Additional contact information
Dieter Hess: University of Cologne
Christoph Müller: University of Cologne

No 2008/28, CFS Working Paper Series from Center for Financial Studies

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.

Keywords: Bayesian Learning; Macroeconomic Announcements; Information Quality; Precision Signals (search for similar items in EconPapers)
JEL-codes: E44 G14 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cba, nep-mac and nep-mst
Date: Written
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Related works:
Working Paper: Price Adjustment to News with Uncertain Precision (2008)
Working Paper: Price Adjustment to News with Uncertain Precision (2008)
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Persistent link: http://EconPapers.repec.org/RePEc:cfs:cfswop:wp200828

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