The discovery and sudden spread of the novel coronavirus (COVID-19) exposed individuals to a great uncertainty about the potential health and economic ramifications of the virus, which triggered a surge in demand for information about COVID-19. To understand financial market implications of individuals’ behavior upon such uncertainty, we explore the relationship between Google search queries related to COVID-19—information search that reflects one’s level of concern or risk perception—and the performance of major financial indices. The empirical analysis based on the Bayesian inference of a structural vector autoregressive model shows that one unit increase in the popularity of COVID-19-related global search queries, after controlling for COVID-19 cases, results in 0.038 – 0.069 % of a cumulative decline in global financial indices after one day and 0.054 – 0.150 % of a cumulative decline after one week.

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The novel coronavirus (COVID‐19) was first identified in China in December 2019. Within a short period of time, the infectious disease has spread far and wide. This study focuses on the distribution of COVID‐19 confirmed cases in China—the original epicentre of the outbreak. We show that the upper tail of COVID‐19 cases in Chinese cities is well described by a power law distribution, with exponent around one in the early phases of the outbreak (when the number of cases was growing rapidly) and less than one thereafter. This finding is significant because it implies that (i) COVID‐19 cases in China is heavy tailed and disperse; (ii) a few cities account for a disproportionate share of COVID‐19 cases; and (iii) the distribution generally has no finite mean or variance. We find that a proportionate random growth model predicated by Gibrat's law offers a plausible explanation for the emergence of a power law in the distribution of COVID‐19 cases in Chinese cities in the early phases of the outbreak.

The novel coronavirus (COVID-19) was first identified in China in December 2019. Within a short period of time, the infectious disease has spread far and wide. This study focuses on the distribution of COVID-19 confirmed cases in China---the original epicenter of the outbreak. We show that the upper tail of COVID-19 cases in Chinese cities is well described by a power law distribution, with exponent less than one, and that a random proportionate growth model predicated by Gibrat's law is a plausible explanation for the emergence of the observed power law behavior. This finding is significant because it implies that COVID-19 cases in China is heavy-tailed and disperse, that a few cities account for a disproportionate share of COVID-19 cases, and that the distribution has no finite mean or variance. The power-law distributedness has implications for effective planning and policy design as well as efficient use of government resources.

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