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

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1016/j.jeconom.2017.10.005

Included in

Econometrics Commons

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