Dynamics and Co-movements Between the COVID-19 Outbreak and the Stock Market in Latin American Countries: An Evaluation Based on the Wavelet-Partial Wavelet Coherence Model.

The COVID-19 outbreak and the global uncertainty it causes produce an apparent panic in stock markets. Efforts to explain the economic spillover effects of COVID-19 can guide authorities to design a control policy against the financial impacts of pandemics. The paper examines the effects of the COVID-19 cases on the stock markets in the emerging Latin American countries of Argentina, Brazil, Chile, Colombia, Mexico, and Peru. The paper employs a continuous partial wavelet methodology to observe lead-lag relations between the daily variables of new COVID-19 cases and the stock market index for each Latin American country. Brazilian new COVID-19 cases led the Bovespa (BVSP) index to decline during the whole period, except February and June 2020, at one month-two month-frequency band. The wavelet and phase difference analyses indicate that, except for Brazil, COVID-19 cases did not affect the stock market indexes adversely during the whole sample period but did affect the stock exchange markets negatively during some sub-sample periods of the entire sample of each country. Dynamics of Latin American stock exchange markets in the short and long run can be explained by some other parameters of real and financial sectors and COVID-19 cases.

Association between temperature and COVID-19 transmission in 153 countries.

The WHO characterized coronavirus disease 2019 (COVID-19) as a global pandemic. The influence of temperature on COVID-19 remains unclear. The objective of this study was to investigate the correlation between temperature and daily newly confirmed COVID-19 cases by different climate regions and temperature levels worldwide. Daily data on average temperature (AT), maximum temperature (MAXT), minimum temperature (MINT), and new COVID-19 cases were collected from 153 countries and 31 provinces of mainland China. We used the spline function method to preliminarily explore the relationship between R0 and temperature. The generalized additive model (GAM) was used to analyze the association between temperature and daily new cases of COVID-19, and a random effects meta-analysis was conducted to calculate the pooled results in different regions in the second stage. Our findings revealed that temperature was positively related to daily new cases at low temperature but negatively related to daily new cases at high temperature. When the temperature was below the smoothing plot peak, in the temperate zone or at a low temperature level (e.g., <25th percentiles), the RRs were 1.09 (95% CI: 1.04, 1.15), 1.10 (95% CI: 1.05, 1.15), and 1.14 (95% CI: 1.06, 1.23) associated with a 1°C increase in AT, respectively. Whereas temperature was above the smoothing plot peak, in a tropical zone or at a high temperature level (e.g., >75th percentiles), the RRs were 0.79 (95% CI: 0.68, 0.93), 0.60 (95% CI: 0.43, 0.83), and 0.48 (95% CI: 0.28, 0.81) associated with a 1°C increase in AT, respectively. The results were confirmed to be similar regarding MINT, MAXT, and sensitivity analysis. These findings provide preliminary evidence for the prevention and control of COVID-19 in different regions and temperature levels.