Volatility During the COVID-19 Pandemic
在均衡框架下研究COVID-19对市场波动的影响，结合信念依赖偏好和随机SEIRD模型，解释了标普500波动率飙升的来源和构成，并评估了不同缓解政策的权衡。
We examine the impact of COVID-19 on market volatility in an equilibrium framework. The model combines beliefs-dependent preferences for economic dynamics and a stochastic Susceptible-Exposed-Infectious-Recovered-Deceased (SEIRD) model with unpredictable birth/vaccine events and mitigating policies for disease propagation. The estimated model explains the realized trajectories of the S&P 500 volatility and number of new cases and identifies the source and composition of the volatility spike while providing a good match for 25 unconditional moments of economic series. Beliefs dependence, in conjunction with real effects due to the short-term decline of the effective workforce early in the pandemic, is critical for this comprehensive explanation of short- and long-run properties. A model comparison study is performed. Out-of-sample volatility prediction exercises document that the good in-sample model fit for volatility and cases is not due to over-parametrization. The effects of alternative mitigation policies such as changes in contamination rate, shelter-in-place duration, and shelter-in-place compliance rate are examined. They document the tradeoff in number of cases and stock volatility during the pandemic, and the dominant role of unemployment news volatility. This paper was accepted by Lukas Schmid, finance. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2024.04352 .