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Asymptotic Inference about Predictive Accuracy Using High Frequency Data

Listed author(s):
  • Jia Li
  • Andrew J. Patton

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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Paper provided by Duke University, Department of Economics in its series Working Papers with number 13-27.

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Length: 70
Date of creation: 2013
Handle: RePEc:duk:dukeec:13-26
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Department of Economics Duke University 213 Social Sciences Building Box 90097 Durham, NC 27708-0097

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Web page: http://econ.duke.edu/

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