Purpose

The purpose of this paper is to explore whether the out-of-sample model bias plays an important role in predicting volatility.

Design/methodology/approach

Under the heterogeneous autoregressive realized volatility (HAR-RV) framework, we analyze the predictive power of out-of-sample model bias for the realized volatility (RV) of the Dow Jones Industrial Average (DJI) and the S&P 500 (SPX) indices from in-sample and out-of-sample perspectives respectively.

Findings

The in-sample results reveal that the prediction model including the model bias can obtain bigger R², and the out-of-sample empirical results based on several evaluation methods suggest that the prediction model incorporating model bias can improve forecast accuracy for the RV of the DJI and the SPX indices. That is, model bias can enhance the predictability of original HAR family models.

Originality/value

The author introduce out-of-sample model bias into HAR family models to enhance model capability in predicting realized volatility.

中文翻译：

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模型偏差在预测波动性中的作用：来自美国股市的证据

目的

本文的目的是探讨样本外模型偏差是否在预测波动率方面发挥重要作用。

设计/方法/途径

在异构自回归已实现波动率 (HAR-RV) 框架下，我们分析了样本外模型偏差对道琼斯工业平均指数 (DJI) 和标准普尔 500 (SPX) 指数的已实现波动率 (RV) 的预测能力分别从样本内和样本外的角度。

发现

样本内结果表明，包含模型偏差的预测模型可以获得较大的R ²，基于多种评价方法的样本外实证结果表明，包含模型偏差的预测模型可以提高RV的预测精度。 DJI 和 SPX 指数。也就是说，模型偏差可以增强原始 HAR 族模型的可预测性。

原创性/价值

作者将样本外模型偏差引入 HAR 族模型，以增强模型预测已实现波动率的能力。