Department of Economics and Management, Beihang University, Beijing, China PR.
PLoS One. 2022 Dec 28;17(12):e0277319. doi: 10.1371/journal.pone.0277319. eCollection 2022.
We analyze whether oil price uncertainty and U.S. stock uncertainty can simultaneously provide additional information to volatility forecast of six major stock indexes. For model settings, we find not only the uncertainty information of previous day, but that of previous week and month will also provide incremental predictive power for the stock market volatility. Based on that, from in-sample and out-of-sample perspective, the empirical evidences imply separately incorporating oil price uncertainty into the model can significantly improve the stock market volatility forecasting performance, but the improvements vanish after controlling the effects of volatility spillover from U.S. stock market while the effect of U.S. stock uncertainty is nonnegligible and sustainable for stock volatility forecasting. We confirm this finding from average and dynamic perspective. We further proceed the process in longer-horizon volatility forecasting, the evidences cannot overturn our conclusion. This conclusion implies that we should be cautious about the stock volatility predictability based on the oil price uncertainty, which further provide some important implications for researchers, regulators and investors.
我们分析了石油价格不确定性和美国股票不确定性是否可以同时为六个主要股票指数的波动预测提供额外信息。在模型设置方面，我们发现不仅前一天的不确定性信息，而且前一周和前一个月的不确定性信息也将为股票市场波动提供额外的预测能力。基于此，从样本内和样本外的角度来看，实证证据表明分别将石油价格不确定性纳入模型可以显著提高股票市场波动的预测性能，但在控制了美国股票市场波动溢出效应的影响后，这种提高就消失了，而美国股票不确定性的影响对于股票波动预测是不可忽视且可持续的。我们从平均和动态的角度证实了这一发现。我们进一步在更长的预测窗口中进行了研究，结果也没有推翻我们的结论。这一结论意味着，我们应该谨慎对待基于石油价格不确定性的股票波动可预测性，这进一步为研究人员、监管机构和投资者提供了一些重要的启示。
