ABSTRACT

This paper offers two main innovations. First, we construct a global fear index (GFI) for the COVID-19 pandemic to support economic, financial, and policy analyses in this area. Second, we demonstrate the application of the index to stock return predictability using OECD data. The panel data predictability results reveal the significance of the index as a good predictor of stock returns during the pandemic. Also, we find that accounting for “asymmetry” effect and macro (common) factors improves the forecast performance of the GFI-based predictive model for stock returns. With regular updates and improvements of the index, several empirical analyses can be extended to other macroeconomic fundamentals in future research.

摘要

本文提供了两个主要的创新。首先，我们为COVID-19大流行建立了全球恐惧指数（GFI），以支持该领域的经济，金融和政策分析。其次，我们使用OECD数据证明了该指数在股票收益可预测性中的应用。面板数据的可预测性结果揭示了该指数作为大流行期间股票回报的良好预测指标的重要性。此外，我们发现考虑“不对称”效应和宏观（共同）因素可以提高基于GFI的股票收益预测模型的预测性能。通过定期更新和改进该指数，可以在未来的研究中将一些经验分析扩展到其他宏观经济基础。