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Asymptotic inference about predictive accuracy using high frequency data
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DOI：10.1016/j.jeconom.2017.10.005.png)
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Abstract
En 中文
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. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
Forecast evaluation
Realized variance
Volatility
Jumps
Semimartingale
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