Impacts of asymmetry on forecasting realized volatility in Japanese stock markets
Impacts of asymmetry on forecasting realized volatility in Japanese stock markets
复制标题
DOI：
10.1016/j.econmod.2021.105533
复制
发表时间：
2021-08
影响因子：
4.7
通讯作者：
Daiki Maki;Y. Ota
中科院分区：
文献类型：
--
作者：
Daiki Maki;Y. Ota
本研究探讨日本股票期货与现货市场中预测已实现波动率最重要的非对称性。我们采用异质自回归（HAR）模型允许三种类型的不对称性：积极和消极的实现半方差（RSV），不对称跳跃，杠杆效应。我们发现一个明显的差异，允许不对称的HAR模型。在非对称模型中，考虑杠杆效应的HAR模型表现最好。此外，具有RSV的HAR模型的预测性能上级具有非对称跳跃的HAR模型。与标准HAR模型相比，非对称跳跃分量并没有产生更好的预测性能。实证结果表明，信息不对称，尤其是杠杆效应和RSV，对日本股市的已实现波动率具有更好的建模效果和更准确的预测效果。
This study investigates the most important asymmetric property for forecasting realized volatility in the Japanese futures and spot stock markets. We employ heterogeneous autoregressive (HAR) models allowing for three types of asymmetry: positive and negative realized semivariance (RSV), asymmetric jumps, and leverage effect. We find a clear difference among HAR models allowing for asymmetry. The HAR model with leverage effect performs best among asymmetric models. Additionally, the forecast performance of the HAR model with RSV is superior to that with asymmetric jumps. The asymmetric jump components do not produce better forecast performance compared with the standard HAR models. The empirical results indicate that asymmetric information, particularly leverage effect and RSV, yields better modeling and more accurate forecast performance for the realized volatility of Japanese stock markets.