Asymptotic Inference about Predictive Accuracy Using High Frequency Data

Jia Li and Andrew Patton

No 13-27, Working Papers from Duke University, Department of Economics

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 (search for similar items in EconPapers)
JEL-codes: C22 C32 C52 C53 C58 (search for similar items in EconPapers)
Pages: 70
Date: 2013
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-mst
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Asymptotic inference about predictive accuracy using high frequency data (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:duk:dukeec:13-26

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