COVID-19 and oil price risk exposure.
Ontology highlight
ABSTRACT:
SUBMITTER: Akhtaruzzaman M
PROVIDER: S-EPMC7718103 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Using U.S. data, we investigate how the COVID-19 pandemic influences oil price returns in an asset pricing framework. Unlike earlier studies, we consider a threshold model to allow for the possibility that COVID-19 risk may not play a role until it reaches a certain level. Based on WTI crude oil spot price data from January 2020 to December 2021, our findings show that oil returns significantly decline with the daily number of COVID-19 deaths but only if the daily death toll exceeds approximately 2100. In addition, a more severe COVID-19 pandemic can substantially increase the exposure of oil returns to various systematic risk factors, which has not been documented in previous literature.
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Project description:The COVID-19 pandemic damaged crude oil markets and amplified the consequences of uncertainty stemming from the Russia-Saudi Arabia oil price war in March-April of 2020. We investigate the impacts of the oil price war on global crude oil markets. By doing so, we use the daily futures and spot prices in three major crude oil markets - West Texas Intermediate, European Brent, and Oman - to perform a systematic analysis of the impacts of the oil price war on them. The event study method, a well-established analytical tool to measure the impacts of a given event on markets, is used in this study. The results indicate that information leakage plays an important role in the impacts of the price war. The outbreak of and truce following the price war have asymmetrical impacts on the markets; negative impacts generated by information leakage during the outbreak are generally more durable than the positive ones it generated during the truce. Furthermore, the magnitude of the impacts on futures markets is negatively correlated with the time-to-maturity of futures. Finally, negative crude oil prices affect West Texas Intermediate crude oil markets the most. Our findings generally show that market participants could perceive and assimilate market changes and adjust their expectations, which restrained the impacts that should have occurred within the oil price war.
| S-EPMC8652835 | biostudies-literature
Project description:This paper studies the impact of the outbreak of coronavirus disease 2019 (COVID-19) on the stock price crash risk of energy firms in China. We find that the stock price crash risk of energy firms significantly decreases in the post-COVID-19 period. We also find that firms that engage in more corporate social responsibility (CSR) activities are less exposed to stock price crash risk in the post-COVID-19 period than those that engage in less CSR activities. Finally, we show that the effect of COVID-19 on stock price crash risk is less severe for state-owned enterprises (SOEs) than for non-SOEs in the post-COVID-19 period. Our findings demonstrate China's economic recovery in the post-COVID-19 period and have policy implications for firms to improve their resilience to exogenous shocks.
| S-EPMC8641492 | biostudies-literature
Project description:BackgroundSelf-perceived exposure risk determines the likelihood of COVID-19 preventive measure compliance to a large extent and is among the most important predictors of mental health problems. Therefore, there is a need to systematically identify important predictors of such risks. This study aims to provide insight into forecasting and understanding risk perceptions and help to adjust interventions that target various social groups in different pandemic phases.MethodsThis study was based on survey data collected from 5001 Norwegians in 2020 and 2021. Interpretable machine learning algorithms were used to predict perceived exposure risks. To detect the most important predictors, the models with best performance were chosen based on predictive errors and explained variances. Shapley additive values were used to examine individual heterogeneities, interpret feature impact and check interactions between the key predictors.ResultsGradient boosting machine exhibited the best model performance in this study (2020: RMSE=.93, MAE=.74, RSQ=.22; 2021: RMSE=.99, MAE=.77, RSQ=.12). The most influential predictors of perceived exposure risk were compliance with interventions, work-life conflict, age and gender. In 2020, work and occupation played a dominant role in predicting perceived risks whereas, in 2021, living and behavioural factors were among the most important predictors. Findings show large individual heterogeneities in feature importance based on people's sociodemographic backgrounds, work and living situations.ConclusionThe findings provide insight into forecasting risk groups and contribute to the early detection of vulnerable people during the pandemic. This is useful for policymakers and stakeholders in developing timely interventions targeting different social groups. Future policies and interventions should be adapted to the needs of people with various life situations.
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Project description: Not available
| S-BSST563 | biostudies-other
Project description:During the COVID-19 pandemic, individuals depended on risk information to make decisions about everyday behaviors and public policy. Here, we assessed whether an interactive website influenced individuals' risk tolerance to support public health goals. We collected data from 11,169 unique users who engaged with the online COVID-19 Event Risk Tool (https://covid19risk.biosci.gatech.edu/) between 9/22/21 and 1/22/22. The website featured interactive elements, including a dynamic risk map, survey questions, and a risk quiz with accuracy feedback. After learning about the risk of COVID-19 exposure, participants reported being less willing to participate in events that could spread COVID-19, especially for high-risk large events. We also uncovered a bias in risk estimation: Participants tended to overestimate the risk of small events but underestimate the risk of large events. Importantly, even participants who voluntarily sought information about COVID risks tended to misestimate exposure risk, demonstrating the need for intervention. Participants from liberal-leaning counties were more likely to use the website tools and more responsive to feedback about risk misestimation, indicating that political partisanship influences how individuals seek and engage with COVID-19 information. Lastly, we explored temporal dynamics and found that user engagement and risk estimation fluctuated over the course of the Omicron variant outbreak. Overall, we report an effective large-scale method for communicating viral exposure risk; our findings are relevant to broader research on risk communication, epidemiological modeling, and risky decision-making.
| S-EPMC10553796 | biostudies-literature
Project description:Although there is increasing awareness of disparities in COVID-19 infection risk among vulnerable communities, the effect of behavioral interventions at the scale of individual neighborhoods has not been fully studied. We develop a method to quantify neighborhood activity behaviors at high spatial and temporal resolutions and test whether, and to what extent, behavioral responses to social-distancing policies vary with socioeconomic and demographic characteristics. We define exposure density ([Formula: see text]) as a measure of both the localized volume of activity in a defined area and the proportion of activity occurring in distinct land-use types. Using detailed neighborhood data for New York City, we quantify neighborhood exposure density using anonymized smartphone geolocation data over a 3-mo period covering more than 12 million unique devices and rasterize granular land-use information to contextualize observed activity. Next, we analyze disparities in community social distancing by estimating variations in neighborhood activity by land-use type before and after a mandated stay-at-home order. Finally, we evaluate the effects of localized demographic, socioeconomic, and built-environment density characteristics on infection rates and deaths in order to identify disparities in health outcomes related to exposure risk. Our findings demonstrate distinct behavioral patterns across neighborhoods after the stay-at-home order and that these variations in exposure density had a direct and measurable impact on the risk of infection. Notably, we find that an additional 10% reduction in exposure density city-wide could have saved between 1,849 and 4,068 lives during the study period, predominantly in lower-income and minority communities.
| S-EPMC8020638 | biostudies-literature
Project description:In this paper we assess the price reaction, performance and volatility timing of European investment funds during the outbreak of Covid-19. We analyze the time period between January and June 2020 and demonstrate that while most of the investment funds exhibit stressed performance, social entrepreneurship funds endured resilience. This performance remained robust during the various stages of evolution of this contagion. The social funds also demonstrated volatility timing that was absent for most of their counterparts. We attribute the overall stability of these funds to their niche investments in social enterprises that specialize in providing innovative solutions for social issues.
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Project description:Individuals infected with the SARS-CoV-2 virus present with a wide variety of phenotypes ranging from asymptomatic to severe and even lethal outcomes. Past research has revealed a genetic haplotype on chromosome 3 that entered the human population via introgression from Neanderthals as the strongest genetic risk factor for the severe COVID-19 phenotype. However, the specific variants along this introgressed haplotype that contribute to this risk and the biological mechanisms that are involved remain unclear. Here, we assess the variants present on the risk haplotype for their likelihood of driving the severe COVID-19 phenotype. We do this by first exploring their impact on the regulation of genes involved in COVID-19 infection using a variety of population genetics and functional genomics tools. We then perform an locus-specific massively parallel reporter assay to individually assess the regulatory potential of each allele on the haplotype in a multipotent immune-related cell line. We ultimately reduce the set of over 600 linked genetic variants to identify 4 introgressed alleles that are strong functional candidates for driving the association between this locus and the severe COVID-19. These variants likely drive the locus’ impact on severity by putatively modulating the regulation of two critical chemokine receptor genes: CCR1 and CCR5. These alleles are ideal targets for future functional investigations into the interaction between host genomics and COVID-19 outcomes.
2023-02-09 | GSE176233 | GEO
Project description:In this study we profiled 288 new serum proteomics samples measured at admission from patients hospitalized within the Mount Sinai Health System with positive SARS-CoV-2 infection. We first computed Th1 and Th2 pathway enrichment scores by gene set variation analysis and then compared the differences in Th2 and Th1 pathway scores between patients that died compared to those that survived.
2022-06-16 | GSE178399 | GEO