Bayesian Learning in Financial Markets - Testing for the Relevance of Information Precision in Price Discovery
Centre for Financial Research (CFR), Working Paper 04-10
37 Pages
Posted: 14 Jun 2005
Last revised: 16 Sep 2010
University of Cologne - Department of Corporate Finance; University of Cologne - Centre for Financial Research (CFR)
University of Vienna - Department of Statistics and Operations Research; Center for Financial Studies (CFS)
There are 2 versions of this paper
Date Written: 2004
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.
Keywords: Bayesian learning, information precision, macroeconomic announcements, asymmetric price response, financial markets, high-frequency data
JEL Classification: E44, G14
Suggested Citation:
Suggested Citation
Hess, Dieter and Hautsch, Nikolaus, Bayesian Learning in Financial Markets - Testing for the Relevance of Information Precision in Price Discovery (2004). Centre for Financial Research (CFR), Working Paper 04-10. Available at SSRN: https://ssrn.com/abstract=741566 or http://dx.doi.org/10.2139/ssrn.741566
