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Price Adjustment to News with Uncertain Precision

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Author Info
Nikolas Hautsch () (School of Business and Economics as well as CASE – Center for Applied Statistics and Economics, Humboldt-Universit¨at zu Berlin)
Dieter Hess () (University of Cologne)
Christoph Müller () (University of Cologne)

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

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Length: 47 pages
Date of creation: 01 Jun 2008
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Handle: RePEc:cfs:cfswop:wp200828

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

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Find related papers by JEL classification:
E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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Full references

Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Enzo Giacomini & Wolfgang Härdle & Volker Krätschmer, 2008. "Dynamic Semiparametric Factor Models in Risk Neutral Density Estimation," SFB 649 Discussion Papers SFB649DP2008-038, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany. [Downloadable!]
  2. Viktor Winschel & Markus Krätzig, 2008. "JBendge: An Object-Oriented System for Solving, Estimating and Selecting Nonlinear Dynamic Models," SFB 649 Discussion Papers SFB649DP2008-034, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany. [Downloadable!]
  3. George J. Jiang & Ingrid Lo & Adrien Verdelhan, 2008. "Information Shocks, Jumps, and Price Discovery -- Evidence from the U.S. Treasury Market," Working Papers 08-22, Bank of Canada. [Downloadable!]
  4. Anton Andriyashin, 2008. "Stock Picking via Nonsymmetrically Pruned Binary Decision Trees," SFB 649 Discussion Papers SFB649DP2008-035, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany. [Downloadable!]
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