The Dynamics of Carbon on Green Energy Equity Investment: Utilizing the Quantile-on-Quantile and Quantile Coherency Approach (preprint)

we analyze the dynamic correlation between the carbon price and the stock returns of green energy companies and calculate the hedging effect of the carbon price on stock returns in green energy sectors. The results show that the coefficients of the carbon price change with time and are vulnerable to extreme events like the COVID-19. The quantile-on-quantile (QQ) model results reveal a dynamic effect from the carbon price to the stock returns of green energy sectors. The quantile coherency (QC) approach results show that investors can benefit more in the short term with high-frequency trading to hedge between carbon trading and the green energy stock market. What’s more, the hedging effects are heterogenetic and investors should adjust their hedging strategies in different quantiles.

Characterization of clinical progression of COVID-19 patients in Shenzhen, China (preprint)

Background: Understanding clinical progression of COVID-19 is a key public health priority that informs resource allocation during an emergency. We characterized clinical progression of COVID-19 and determined important predictors for faster clinical progression to key clinical events and longer use of medical resources. Methods and Findings: The study is a single-center, observational study with prospectively collected data from all 420 patients diagnosed with COVID-19 and hospitalized in Shenzhen between January 11th and March 10th, 2020 regardless of clinical severity. Using competing risk regressions according to the methods of Fine and Gray, we found that males had faster clinical progression than females in the older age group and the difference could not be explained by difference in baseline conditions or smoking history. We estimated the proportion of cases in each severity stage over 80 days following symptom onset using a nonparametric method built upon estimated cumulative incidence of key clinical events. Based on random survival forest models, we stratified cases into risk sets with very different clinical trajectories. Those who progressed to the severe stage (22%,93/420), developed acute respiratory distress syndrome (9%,39/420), and were admitted to the intensive care unit (5%,19/420) progressed on average 9.5 days (95%CI 8.7,10.3), 11.0 days (95%CI 9.7,12.3), and 10.5 days (95%CI 8.2,13.3), respectively, after symptom onset. We estimated that patients who were admitted to ICUs remained there for an average of 34.4 days (95%CI 24.1,43.2). The median length of hospital stay was 21.3 days (95%CI, 20.5,22.2) for cases who did not progress to the severe stage, but increased to 52.1 days (95%CI, 43.3,59.5) for those who required critical care. Conclusions: Our analyses provide insights into clinical progression of cases starting early in the course of infection. Patient characteristics near symptom onset both with and without lab parameters have tremendous potential for predicting clinical progression and informing strategic response.