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.
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