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Bayesian Learning in Financial Markets: Testing for the Relevance of Information Precision in Price Discovery

Author

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  • Nikolaus Hautsch

    (Institute of Economics, University of Copenhagen)

  • Dieter Hess

    (University of Cologne)

Abstract

An important claim of Bayesian learning and a standard assumption in price discovery models is that the strength of the price impact of unanticipated information depends on the precision of the news. In this paper, we test for this assumption by analyzing intra-day price responses of CBOT T-bond futures to U.S. employment announcements. By employing additional detail information besides the widely used headline figures, we extract release-specific precision measures which allow to test for the claim of Bayesian updating. We find that the price impact of more precise information is significantly stronger. The results remain stable even after controlling for an asymmetric price response to 'good' and 'bad' news.

Suggested Citation

  • Nikolaus Hautsch & Dieter Hess, 2004. "Bayesian Learning in Financial Markets: Testing for the Relevance of Information Precision in Price Discovery," Discussion Papers 04-17, University of Copenhagen. Department of Economics.
    • Handle: RePEc:kud:kuiedp:0417
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Bayesian learning; information precision; macroeconomic announcements; asymmetric price response; ¯nancial markets; high-frequency data;
    All these keywords.

    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; Insider Trading

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