Asymptotic Inference about Predictive Accuracy Using High Frequency Data
This paper provides a general framework that enables many existing inference methods for predictive accuracy to be used in applications that involve forecasts of latent target variables. Such applications include the forecasting of volatility, correlation, beta, quadratic variation, jump variation, and other functionals of an underlying continuous-time process. We provide primitive conditions under which a "negligibility" result holds, and thus the asymptotic size of standard predictive accuracy tests, implemented using a high-frequency proxy for the latent variable, is controlled. An extensive simulation study verifies that the asymptotic results apply in a range of empirically relevant applications, and an empirical application to correlation forecasting is presented.
|Date of creation:||2013|
|Contact details of provider:|| Postal: Department of Economics Duke University 213 Social Sciences Building Box 90097 Durham, NC 27708-0097|
Phone: (919) 660-1800
Fax: (919) 684-8974
Web page: http://econ.duke.edu/
When requesting a correction, please mention this item's handle: RePEc:duk:dukeec:13-26. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Department of Economics Webmaster)
If references are entirely missing, you can add them using this form.