This paper proposes an extended conditional autoregressive range (EXCARR) model to describe the range-based volatility dynamics of financial assets. Our EXCARR model not only takes the conditional autoregressive range (CARR) model as a special case but also considers the asymmetry between the upward range and the downward range. Empirical studies performed on a variety of stock indexes show that the EXCARR model outperforms not only the CARR model but also the asymmetric CARR (ACARR) model in both in-sample and out-of-sample forecasting. Hence, our EXCARR model provides a new benchmark for range-based volatility forecasting.

中文翻译：

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基于范围的波动率预测：扩展的条件自回归范围模型

本文提出了一个扩展的条件自回归范围（EXCARR）模型来描述金融资产基于范围的波动动态。我们的 EXCARR 模型不仅将条件自回归范围 (CARR) 模型作为特例，而且还考虑了上行范围和下行范围之间的不对称性。对各种股票指数进行的实证研究表明，EXCARR 模型不仅优于 CARR 模型，而且在样本内和样本外预测方面都优于非对称 CARR (ACARR) 模型。因此，我们的 EXCARR 模型为基于范围的波动率预测提供了新的基准。