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    Asymptotic Inference about Predictive Accuracy Using High Frequency Data

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    558.9 Kb
    Date
    2013-07-06
    Authors
    Li, J
    Patton, Andrew J
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    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
    Journal article
    Subject
    Forecast evaluation
    realized variance
    volatility
    jumps
    semimartingale
    Permalink
    https://hdl.handle.net/10161/13188
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    Scholars@Duke

    Patton

    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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