Asymptotic Inference about Predictive Accuracy Using High Frequency Data
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.
Type
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https://hdl.handle.net/10161/13188Collections
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Show full item recordScholars@Duke
Andrew J. Patton
Zelter Family Professor
Patton’s research interests lie in financial econometrics, with an emphasis on forecasting
volatility and dependence, forecast evaluation methods, and the analysis of hedge
funds and mutual funds. His research has appeared in a variety of academic journals,
including the Journal of Finance, Journal of Econometrics, Journal of Financial Economics,
Journal of the American Statistical Association, Review of Financial Studies, and
the Journal of Business and Economic Statistics. He has gi
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