Abstract
Firms suspended dividend payments in unprecedented numbers and at unparalleled speed in response to the outbreak of the COVID-19 pandemic. We develop a dynamic econometric model that allows dividend suspensions to affect the conditional mean, volatility, and jump probability of growth in daily dividends and demonstrate how the parameters of this model can be estimated using Bayesian Gibbs sampling methods. We find that dividend suspensions had a sharp and immediate impact on the conditional mean and volatility of daily dividend growth. Information embedded in daily dividend suspensions proves valuable in monitoring and predicting the trajectory of broader measures of economic activity during the pandemic.
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