Publication Type
Journal Article
Version
acceptedVersion
Publication Date
4-2018
Abstract
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.
Keywords
Forecast evaluation, Realized variance, Volatility, Jumps, Semimartingale
Discipline
Econometrics
Research Areas
Econometrics
Publication
Journal of Econometrics
Volume
203
Issue
2
First Page
223
Last Page
240
ISSN
0304-4076
Identifier
10.1016/j.jeconom.2017.10.005
Publisher
Elsevier
Citation
LI, Jia and PATTON, Andrew J..
Asymptotic inference about predictive accuracy using high frequency data. (2018). Journal of Econometrics. 203, (2), 223-240.
Available at: https://ink.library.smu.edu.sg/soe_research/2583
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/j.jeconom.2017.10.005