Correcting the Errors: Volatility Forecast Evaluation Using High-Frequency Data and Realized Volatilities
提出一种无模型调整方法，利用高频数据中的已实现波动率基准，修正波动率预测评估中的测量误差，从而更准确地估计波动率的可预测性。
We develop general model-free adjustment procedures for the calculation of unbiased volatility loss functions based on practically feasible realized volatility benchmarks. The procedures, which exploit recent nonparametric asymptotic distributional results, are both easy-to-implement and highly accurate in empirically realistic situations. We also illustrate that properly accounting for the measurement errors in the volatility forecast evaluations reported in the existing literature can result in markedly higher estimates for the true degree of return volatility predictability. Copyright The Econometric Society 2005.