Nonlinearity and forecast performance of realized volatility
Nonlinearity and forecast performance of realized volatility
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DOI：
10.1080/23737484.2023.2175277
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发表时间：
2023-01
期刊：
影响因子：
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通讯作者：
Daiki Maki
中科院分区：
文献类型：
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作者：
Daiki Maki
摘要本研究探讨考虑已实现波动率的非线性是否会导致更好的预测绩效。本文提出了一种新的考虑非线性因素的可实现波动率预测模型，而不需要假设特定的非线性模型。提出的模型使用泰勒级数近似方法来解释非线性。我们将其应用于美国、日本、英国和中国的代表性股票指数的已实现波动率，并观察了它们的样本内非线性。此外，我们评估样本外预测性能。实证结果表明，实际波动率具有非线性，所提模型的预测效果优于标准模型。
Abstract This study examines whether accounting for the nonlinearity of realized volatility leads to better forecast performance. We propose a new realized volatility forecasting model that considers nonlinearities without the assumption of a particular nonlinear model. The proposed model uses the Taylor series approximation method to account for nonlinearities. We applied it to the realized volatility of representative stock indices from the U.S., Japan, the U.K., and China and observed their in-sample nonlinearities. Additionally, we evaluate out-of-sample forecast performance. The empirical results show that realized volatility has nonlinearity, and the proposed models exhibit better forecast performance than standard models.