In this paper we evaluate the in-sample fit and out-of-sample forecasts of various combinations of realized variance models and estimation criteria . Our empirical findings highlight that: independently of the econometrician’s forecasting loss function, certain estimation criteria perform significantly better than others; the simple ARMA modeling of the log realized variance generates superior forecasts than the HAR family, for any of the forecasting loss functions considered; the (2,1) parameterizations with negative lag-2 coefficient emerge as the benchmark specifications generating the best forecasts and approximating long-run dependence as well as the HAR family.

中文翻译：

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实现的方差建模：将预测与估计分离*

在本文中，我们评估了已实现方差模型和估计标准的各种组合的样本内拟合和样本外预测。我们的经验发现表明：与计量经济学家的预测损失函数无关，某些估算标准的性能明显优于其他标准；对于所考虑的任何预测损失函数，对数实现方差的简单ARMA建模比HAR系列产生的预测更好；具有滞后2系数的（2,1）参数化作为基准规范出现，产生了最佳的预测并近似于长期依赖关系以及HAR系列。