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Price adjustment to news with uncertain precision

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Author Info

  • Hautsch, Nikolaus
  • Hess, Dieter E.
  • Müller, Christoph

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. --

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Bibliographic Info

Paper provided by Center for Financial Studies (CFS) in its series CFS Working Paper Series with number 2008/28.

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Date of creation: 2008
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Handle: RePEc:zbw:cfswop:200828

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Keywords: Bayesian Learning; Macroeconomic Announcements; Information Quality; Precision Signals;

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References

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Citations

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Cited by:
  1. Hautsch, Nikolaus & Hess, Dieter & Veredas, David, 2011. "The impact of macroeconomic news on quote adjustments, noise, and informational volatility," Journal of Banking & Finance, Elsevier, vol. 35(10), pages 2733-2746, October.
  2. Hess, Dieter & Orbe, Sebastian, 2011. "Irrationality or efficiency of macroeconomic survey forecasts? Implications from the anchoring bias test," CFR Working Papers 11-13, University of Cologne, Centre for Financial Research (CFR).

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