Price adjustment to news with uncertain precision
- 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.
| Author: | Nikolaus HautschORCiDGND, Dieter Hess, Christoph Müller |
|---|---|
| URN: | urn:nbn:de:hebis:30-57665 |
| Parent Title (German): | Center for Financial Studies (Frankfurt am Main): CFS working paper series ; No. 2008,28 |
| Series (Serial Number): | CFS working paper series (2008, 28) |
| Document Type: | Working Paper |
| Language: | English |
| Year of Completion: | 2008 |
| Year of first Publication: | 2008 |
| Publishing Institution: | Universitätsbibliothek Johann Christian Senckenberg |
| Release Date: | 2008/09/24 |
| HeBIS-PPN: | 205704204 |
| Institutes: | Wissenschaftliche Zentren und koordinierte Programme / Center for Financial Studies (CFS) |
| Dewey Decimal Classification: | 3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft |
| Licence (German): |  Deutsches Urheberrecht |
