Asymptotic Inference about Predictive Accuracy Using High Frequency Data

dc.contributor.author

Li, J

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Patton, AJ

dc.date.accessioned

2016-12-06T15:07:36Z

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2013-07-06

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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.

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https://hdl.handle.net/10161/13188

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Elsevier BV

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Economic Research Initiatives at Duke (ERID) Working Paper

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Forecast evaluation

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realized variance

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volatility

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jumps

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semimartingale

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Asymptotic Inference about Predictive Accuracy Using High Frequency Data

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Journal article

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163

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Source info: Economic Research Initiatives at Duke (ERID) Working Paper No. 163

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Duke

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Economics

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Trinity College of Arts & Sciences

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