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 2008/01, FRU Working Papers from University of Copenhagen. Department of Economics. Finance Research Unit

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 disclosed. 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: prediction Bayesian learning; macroeconomic announcements; information quality; precision signals (search for similar items in EconPapers)
JEL-codes: E44 G14 (search for similar items in EconPapers)
Pages: 40 pages
Date: 2008-06
New Economics Papers: this item is included in nep-cba, nep-mac and nep-mst
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Citations: View citations in EconPapers (4)

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Related works:
Journal Article: Price adjustment to news with uncertain precision (2012)
Working Paper: Price adjustment to news with uncertain precision (2011)
Working Paper: Price adjustment to news with uncertain precision (2008)
Working Paper: Price adjustment to news with uncertain precision (2008)
Working Paper: Price adjustment to news with uncertain precision (2008)
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