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2.
PLoS One ; 17(2): e0259869, 2022.
Article in English | MEDLINE | ID: covidwho-1690763

ABSTRACT

The purpose of our study is to figure out the transitions of the cryptocurrency market due to the outbreak of COVID-19 through network analysis, and we studied the complexity of the market from different perspectives. To construct a cryptocurrency network, we first apply a mutual information method to the daily log return values of 102 digital currencies from January 1, 2019, to December 31, 2020, and also apply a correlation coefficient method for comparison. Based on these two methods, we construct networks by applying the minimum spanning tree and the planar maximally filtered graph. Furthermore, we study the statistical and topological properties of these networks. Numerical results demonstrate that the degree distribution follows the power-law and the graphs after the COVID-19 outbreak have noticeable differences in network measurements compared to before. Moreover, the results of graphs constructed by each method are different in topological and statistical properties and the network's behavior. In particular, during the post-COVID-19 period, it can be seen that Ethereum and Qtum are the most influential cryptocurrencies in both methods. Our results provide insight and expectations for investors in terms of sharing information about cryptocurrencies amid the uncertainty posed by the COVID-19 pandemic.


Subject(s)
COVID-19/epidemiology , Investments/trends , Models, Economic , COVID-19/economics , Humans , Information Dissemination , Investments/statistics & numerical data , Pandemics/economics , Uncertainty
3.
Front Public Health ; 9: 661482, 2021.
Article in English | MEDLINE | ID: covidwho-1389256

ABSTRACT

This paper examines the effects of pandemic uncertainty on socially responsible investments. We use the overall corporate sustainability performance index in the Global-100 Most Sustainable Corporations in the World dataset to measure socially responsible investments. The global pandemic uncertainty is also measured by the World Pandemic Uncertainty Index. We focus on the panel dataset from 2012 to 2020, and the results show that the World Pandemic Uncertainty Index is positively related to socially responsible investments. The main findings remain significant when we utilize various panel estimation techniques.


Subject(s)
COVID-19/economics , Investments/economics , Investments/statistics & numerical data , Models, Economic , Pandemics/statistics & numerical data , Social Responsibility , Uncertainty , Humans , SARS-CoV-2
4.
PLoS One ; 16(2): e0246235, 2021.
Article in English | MEDLINE | ID: covidwho-1081810

ABSTRACT

This study reports on the application of a Portfolio Decision Analysis (PDA) to support investment decisions of a non-profit funder of vaccine technology platform development for rapid response to emerging infections. A value framework was constructed via document reviews and stakeholder consultations. Probability of Success (PoS) data was obtained for 16 platform projects through expert assessments and stakeholder portfolio preferences via a Discrete Choice Experiment (DCE). The structure of preferences and the uncertainties in project PoS suggested a non-linear, stochastic value maximization problem. A simulation-optimization algorithm was employed, identifying optimal portfolios under different budget constraints. Stochastic dominance of the optimization solution was tested via mean-variance and mean-Gini statistics, and its robustness via rank probability analysis in a Monte Carlo simulation. Project PoS estimates were low and substantially overlapping. The DCE identified decreasing rates of return to investing in single platform types. Optimal portfolio solutions reflected this non-linearity of platform preferences along an efficiency frontier and diverged from a model simply ranking projects by PoS-to-Cost, despite significant revisions to project PoS estimates during the review process in relation to the conduct of the DCE. Large confidence intervals associated with optimization solutions suggested significant uncertainty in portfolio valuations. Mean-variance and Mean-Gini tests suggested optimal portfolios with higher expected values were also accompanied by higher risks of not achieving those values despite stochastic dominance of the optimal portfolio solution under the decision maker's budget constraint. This portfolio was also the highest ranked portfolio in the simulation; though having only a 54% probability of being preferred to the second-ranked portfolio. The analysis illustrates how optimization modelling can help health R&D decision makers identify optimal portfolios in the face of significant decision uncertainty involving portfolio trade-offs. However, in light of such extreme uncertainty, further due diligence and ongoing updating of performance is needed on highly risky projects as well as data on decision makers' portfolio risk attitude before PDA can conclude about optimal and robust solutions.


Subject(s)
Infection Control/economics , Investments/statistics & numerical data , Vaccines/economics , Uncertainty
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