Implications of Stochastic Transmission Rates for Managing Pandemic Risks
Harrison Hong, Neng Wang, Jinqiang Yang
Abstract
Abstract We introduce aggregate transmission shocks to an epidemic model and link firm valuations to infections via an asset pricing framework with vaccines. Infections lower earnings growth but firms can mitigate damages. We estimate a large reproduction number ${\mathcal R}_0$ and transmission volatility for COVID-19. Using these estimates, we quantify the bias of deterministic approximations based on ${\mathcal R}_0$. Our model generates predictions consistent with the data: unexpected infection resurgence, nonmonotonic mitigation policies, and higher price-to-earnings ratios during a pandemic. Valuations would be significantly lower absent mitigation and a high vaccine arrival rate.
Topics & Concepts
EarningsVolatility (finance)EconometricsTransmission (telecommunications)EconomicsDamagesPandemicCoronavirus disease 2019 (COVID-19)Asset (computer security)Aggregate (composite)Monetary economicsComputer scienceFinanceInfectious disease (medical specialty)TelecommunicationsPolitical scienceComputer securityPathologyDiseaseMaterials scienceComposite materialLawMedicineCOVID-19 epidemiological studiesCOVID-19 Pandemic ImpactsAgricultural risk and resilience