Price Adjustment to News with Uncertain Precision
49 Pages Posted: 4 Mar 2007 Last revised: 29 Mar 2012
Date Written: November 1, 2010
We analyze how markets adjust to new information when the reliability of news is uncertain and has to be estimated itself. We propose a Bayesian learning model where market participants receive fundamental information along with noisy estimates of news' precision. It is shown that the efficiency of a precision estimate drives the shape and the location of price response functions to news. Increasing estimation errors induce stronger nonlinearities in price responses. Analyzing high-frequency reactions of T-bond futures prices to employment releases, we find strong empirical support for the model's predictions and show that the consideration of precision uncertainty is statistically and economically important.
Keywords: Bayesian learning, information quality, precision signals, macroeconomic announcements
JEL Classification: E44, G14
Suggested Citation: Suggested Citation