论文标题
不对称的影响对日本股市建模和预测实现的波动性
The impacts of asymmetry on modeling and forecasting realized volatility in Japanese stock markets
论文作者
论文摘要
这项研究调查了不对称性对日本期货和现货股票市场中实现波动率的建模和预测的影响。我们采用异质自回旋（HAR）模型，允许三种类型的不对称性：正面和负面的半变量（RSV），不对称跳跃和杠杆作用。估计结果表明，杠杆作用明显影响实现波动率模型的建模。 Nikkei 225中的现场和期货市场都存在杠杆作用。尽管实现的半变量有助于更好的建模，但RSV模型的估计取决于这些模型是否具有杠杆作用。不对称跳跃组件对实现的波动率模型没有明显的影响。虽然杠杆效应和实现的半发行也可以提高挥发性模型的样本外预测性能，但不对称跳跃对于预测能力并不有用。这项研究的经验结果表明，不对称信息，尤其是利用效果和实现的半发性，可以更好地建模和更准确的预测性能。因此，当我们对我们进行建模并预测日本股票市场的实现波动时，应包括不对称信息。
This study investigates the impacts of asymmetry on the modeling and forecasting of 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 effects. The estimation results show that leverage effects clearly influence the modeling of realized volatility models. Leverage effects exist for both the spot and futures markets in the Nikkei 225. Although realized semivariance aids better modeling, the estimations of RSV models depend on whether these models have leverage effects. Asymmetric jump components do not have a clear influence on realized volatility models. While leverage effects and realized semivariance also improve the out-of-sample forecast performance of volatility models, asymmetric jumps are not useful for predictive ability. The empirical results of this study indicate that asymmetric information, in particular, leverage effects and realized semivariance, yield better modeling and more accurate forecast performance. Accordingly, asymmetric information should be included when we model and forecast the realized volatility of Japanese stock markets.
