A Survey Investigating the Key Factors and Theoretical E-Learning Frameworks During Crisis Periods

In the previous ten years, there has been an astounding expansion in the study and application of e-learning frameworks. The recent literature and e-learning ideas were investigated in this study, with e-learning research's different parameters being précised. E-learning processes' associated services, technology, as well as stakeholders, are the three principal aspects of e-learning systems. A typology of services comprising e-learning models is presented in a framework, with stakeholders, technology, and learning approaches being included. Accordingly, the aforementioned aspects are considered through a detailed literature review, with e-learning frameworks' relationship with the different classified stakeholder groups also clarified. Finally, ways to resolve the foremost challenges identified through the literature review are posed, with our e-learning system also presented. Furthermore, the proposed answer may direct and facilitate the appropriate appraisal of learners, educators, and educational facilities by decision-makers, drawing on data provided through live interaction.

Comparing the asymmetric efficiency of dirty and clean energy markets pre and during COVID-19.

In the backdrop of the recent COVID-19 pandemic, the study examines the comparative asymmetric efficiency of dirty and clean energy markets pre and during the COVID-19 pandemic. For this purpose, we utilize an asymmetric multifractality detrended fluctuation analysis (A-MF-DFA). The study's findings uncover the presence of asymmetric multifractality in clean and dirty energy markets. In addition, multifractality in the energy markets is sensitive to trends, time horizon and major events. More importantly, the results suggest superior efficiency of clean-energy markets compared to conventional energies. We confirm the time-varying nature of market efficiency in the energy markets, and during the recent COVID-19 outbreak, market inefficiencies in the clean and dirty energy markets soared. In this way, the study holds meaningful insights for policymakers, energy policy practitioners, investors, and financial market participants to choose between clean (dirty) investments based on their asymmetric efficiency (inefficiency).

Modelling extreme risk spillovers in the commodity markets around crisis periods including COVID19

In this paper, we examine extreme spillovers among the realized volatility of various energy, metals, and agricultural commodities over the period from September 23, 2008, to June 1, 2020. Using high-frequency (5-min) price data on commodity futures, we compute daily realized volatility and then apply quantile-based connectedness measures. The results show that the connectedness measures estimated at the lower and upper quantiles are much higher than those estimated at the median, implying that realized volatility shocks circulate more intensely during extreme events relative to normal periods, which endangers the stability of the system of volatility connectedness under extreme events such as the COVID19 outbreak. There is evidence of a strong asymmetry between the behaviour of volatility spillovers in lower and upper quantiles, given that the connectedness measures estimated at the upper quantile are the highest. The main results are robust to rolling window size and other alternative choices. Our analyses matter to investors and policy makers who are concerned with the stability of commodity markets.