Capturing the timing of crisis evolution: A machine learning and directional wavelet coherence approach to isolating event-specific uncertainty using Google searches with an application to COVID-19
构建基于谷歌搜索的机器学习指数，分离COVID-19相关不确定性，并用定向小波分析研究疫情对金融市场不确定性的动态影响，帮助政策制定者和研究者精准识别危机阶段。
The phases of a crisis are critical to understanding its evolution. We construct an economic agent-determined machine learning-based Google search index that associates search terms with uncertainty to isolate COVID-19-related uncertainty from overall uncertainty. Subsequently, we apply directional wavelet analysis that discriminates between positive and negative associations to study the evolving impact of the COVID-19 pandemic on financial market uncertainty and financial markets. Our approach permits us to delineate crisis phases with high precision according to information type. The analysis that follows suggests that policy responses impacted uncertainty and that the novelty of the COVID-19 outbreak had a significant impact on global stock markets. Regression analysis, wavelet entropy and partial wavelet coherence confirm the informational content of our uncertainty index. The approach presented in this study is applied to the COVID-19 crisis but is generalisable beyond the pandemic and can assist in decision-making during times of economic and financial market turmoil and should be of interest to policymakers, researchers and econometricians. • We model the evolving impact of the COVID-19 crisis on financial market uncertainty. • We use an economic agent-determined machine learning-based Google search index. • Directional wavelet coherence is applied to delineate COVID-19 crisis phases with high precision. • Our approach is generalisable and can assist in facilitating policy and investment decision making. • Our study presents a novel way of identifying crisis phases focusing on changing informational contribution to uncertainty.