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Price Adjustment to News with Uncertain Precision

Listed author(s):
  • Nikolaus Hautsch

    (Humboldt-Universität zu Berlin)

  • Dieter Hess

    (University of Cologne)

  • Christoph Müller

    (University of Cologne)

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.

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File URL: http://www.econ.ku.dk/fru/WorkingPapers/PDF/2008/200801.pdf
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Paper provided by University of Copenhagen. Department of Economics. Finance Research Unit in its series FRU Working Papers with number 2008/01.

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Length: 40 pages
Date of creation: Jun 2008
Handle: RePEc:kud:kuiefr:200801
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Web page: http://www.econ.ku.dk/FRU/
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