The nCOVID-19 and financial stress in the USA: health is wealth

COVID-19 and stock returns: Evidence from the Markov switching dependence approach

This paper aims to investigate the regime-switching and time-varying dependence between the COVID-19 pandemic and the US stock markets using a Markov-switching framework. It makes two contributions to the empirical literature by showing that: (a) the variations of the daily reported COVID-19 cases and cumulative COVID-19 deaths induced asymmetric lower (left) and upper (right) tail dependence with the stock markets, and its left and right tail dependence exhibited significant time-varying trends; and (b) the left and right tail dependence between the stock markets and the pandemic exhibited significant regime-switching behaviours, with its switching probabilities in the higher tail dependence stage all being greater than in the lower tail dependence stage after 1 December 2019. Moreover, given that there is concurrent but significant financial market reaction to any unexpected emergence of a transmittable respirational disease or a natural calamity, the outcomes have some vital implications to market players and policymakers.

Development and Validation of the Multidimensional COVID-19 Vaccine Hesitancy Scale

The aim of the study was to develop and validate the Multidimensional Covid-19 Vaccine Hesitancy Scale (CoVaH), a self-report measure to assess the beliefs and attitudes beneath vaccination hesitancy and reasons for vaccine refusal in the context of Covid-19. A sample of 1503 Hungarian respondents filled out the scale. Exploratory and confirmatory factor analysis was conducted to identify latent constructs underlying participants' responses. Findings show a robust three-factor solution for the 15-item CoVaH with high factor loadings on each factor: skepticism, risk perception and fear of Covid-19 vaccine. The CoVaH displayed very good fit indices (KMO = .94, RMSEA = 0.049, CFI = .983) and internal consistencies (α values > .89) and was found to have proper convergent, concurrent and discriminant validity in identifying Covid-19 vaccine hesitancy in the general population. The new scale adds to the literature through the identification of the fear of COVID-19 vaccines, as a newly highlighted explanatory variable of COVID-19 vaccine hesitancy, besides the other formerly identified components. The scale, available in English and Hungarian, allows the assessment of vaccine uptake hesitancy and has the potential to help targeted interventions, considering individual factors that interfere with vaccination acceptance.

Research on China's Monetary Policy Orientation and Regulation in COVID-19

The outbreak of COVID-19 in 2019 has caused a huge impact on the global economy. In this context, it is of great significance to study the orientation and regulation of China's monetary policy, which aims to mitigate the external impact brought by COVID-19. Therefore, this paper uses the SV-TVP-FAVAR model to analyze the dynamic relationships among interest rate, inflation gap and output gap. The main conclusions are as follows. First, the output gap has a significant impact on the adjustment of the interest rate and inflation gap. In the COVID-19 era, the former response is positive and the latter response is negative. Second, the impact of the inflation gap on the interest rate fluctuates frequently, but the impact has gradually weakened in recent years. In addition, the inflation gap shows a significant positive response to the impact of the output gap. Third, interest rate is characterized by targeting the output gap and the inflation gap in the short term. However, in the period of COVID-19, the regulation effect of China's monetary policy on the inflation gap and the output gap has weakened. Meanwhile, compared with targeting the output gap, monetary policy has a more obvious orientation to control inflation.

Appraising the oil-stock nexus during the COVID-19 pandemic shock: a panel threshold analysis

We examine the oil-stock nexus in 24 countries amidst the COVID-19 pandemic and test for threshold effects on oil prices using Hansen (1999) panel dynamic threshold model and recent extensions of Kremer et al. (2013) and Seo and Shin (2016). We find evidence of nonlinearities and threshold effects in oil prices. As an addition to literature, our estimated model shows that stock market prices react in a regime-style manner, when the joint effects of oil prices, exchange rate changes, number of reported cases, and the number of death due to COVID-19 pandemic are analyzed. This is in support of the theoretical model of investor sentiment by Barberis et al. (1998). Therefore, we are of the opinion that policymakers, governments, and investors in their business decision-making process should put into consideration and also observe changes in the global reported cases alongside the number of deaths and how oil prices are evolving, as the global economy is further affected by the COVID-19 pandemic shock.

COVID-19 pandemic and economic policy uncertainty regimes affect commodity market volatility

This paper investigates the switching effect of COVID-19 pandemic and economic policy uncertainty on commodity prices. We employ Markov regime-switching dynamic model to explore price regime dynamics of eight widely traded commodities namely oil, natural gas, corn, soybeans, silver, gold, copper, and steel. We fit two Markov switching regimes to allow parameters to respond to both low and high volatilities. The empirical evidence shows oil, natural gas, corn, soybean, silver, gold, copper, and steel returns adjust to shocks in COVID-19 outcomes and economic policy uncertainty at varying degrees--in both low volatility and high volatility regimes. In contrast, oil and natural gas do not respond to changes in COVID-19 deaths in both regimes. The findings show most commodities are responsive to historical price in terms of demand and supply in both volatility regimes. Our findings further show a high probability that commodity prices will remain in low volatility regime than in high volatility regime--owing to COVID-19-attributed market uncertainties. These findings are useful to both investors and policymakers--as precious metals and agricultural commodities show less negative response to exogenous variables. Thus, investors and portfolio managers could use precious metals, viz. Gold for short-term cover against systematic risks in the market during the period of global pandemic.

Assessing a fossil fuels externality with a new neural networks and image optimisation algorithm: the case of atmospheric pollutants as confounders to COVID-19 lethality

This paper demonstrates how the combustion of fossil fuels for transport purpose might cause health implications. Based on an original case study [i.e. the Hubei province in China, the epicentre of the coronavirus disease-2019 (COVID-19) pandemic], we collected data on atmospheric pollutants (PM2.5, PM10 and CO2) and economic growth (GDP), along with daily series on COVID-19 indicators (cases, resuscitations and deaths). Then, we adopted an innovative Machine Learning approach, applying a new image Neural Networks model to investigate the causal relationships among economic, atmospheric and COVID-19 indicators. Empirical findings emphasise that any change in economic activity is found to substantially affect the dynamic levels of PM2.5, PM10 and CO2 which, in turn, generates significant variations in the spread of the COVID-19 epidemic and its associated lethality. As a robustness check, the conduction of an optimisation algorithm further corroborates previous results.

COVID-19 vaccine-taking hesitancy among Bangladeshi people: knowledge, perceptions and attitude perspective

Several novel efforts have been put forth to make a readily available vaccine against the global pandemic of COVID-19. However, there seems to appear vaccine-taking hesitancy among the general people. Against this backdrop, this current study sets to assess the vaccine-taking intention, ways to overcome the vaccine-taking reluctance among Bangladeshi people and explore their knowledge, perceptions, and attitude toward the COVID-19 vaccine. To this end, this study leveraged on a cross-sectional survey, which was consisted of 1377 respondents covering the eight divisions of Bangladesh. The descriptive statistical method and ordinal logistics regression were employed to explore and rationalize our study outlined objectives. Empirical findings revealed that approximately 71% of the respondents had adequate knowledge about the COVID-19 vaccine, whereas 46% of the respondents were willing to be vaccinated against COVID-19 while the rest of the respondents were hesitant to take the vaccine. However, concern about the potential side effects was one of the core reasons for vaccine-taking hesitancy. Assuring the common people about vaccine safety and efficacy, along with easing the registration procedure, can ameliorate people's confidence to get vaccinated. Meanwhile, about 60% of the respondents believed that a vaccine could help Bangladesh win the battle against COVID-19 and will allow back to normal life. Although the government has taken some pragmatic action steps to promote the vaccination rate, it is recommended that the mass vaccination program should be extended to the grassroots level with proper extension community support and easing the registration process.

Impact of the COVID-19 outbreak on the US equity sectors: Evidence from quantile return spillovers

The aim of this study is to examine the extreme return spillovers among the US stock market sectors in the light of the COVID-19 outbreak. To this end, we extend the now-traditional Diebold-Yilmaz spillover index to the quantiles domain by building networks of generalized forecast error variance decomposition of a quantile vector autoregressive model specifically for extreme returns. Notably, we control for common movements by using the overall stock market index as a common factor for all sectors and uncover the effect of the COVID-19 outbreak on the dynamics of the network. The results show that the network structure and spillovers differ considerably with respect to the market state. During stable times, the network shows a nice sectoral clustering structure which, however, changes dramatically for both adverse and beneficial market conditions constituting a highly connected network structure. The pandemic period itself shows an interesting restructuring of the network as the dominant clusters become more tightly connected while the rest of the network remains well separated. The sectoral topology thus has not collapsed into a unified market during the pandemic.

COVID-19 and Spillover Effect of Global Economic Crisis on the United States' Financial Stability

Due to the novel coronavirus pandemic (COVID-19), the lockdown engendered has had a vicious impact on the global economy. This analysis' prime intention is to evaluate the impact of the United States' economic and health crisis as a result of COVID-19 on its financial stability. Additionally, the investigation analyzed the spillover impact of the worldwide economic slowdown experienced by COVID-19 on the United States' financial volatility. The study applied an autoregressive distributed lag (ARDL) model and discovered that the economic and health crises that occurred in the United States portentously upset the future expectations of its investors. Conspicuously, the health crisis in Spain and Italy were ominous spillovers of the United States' financial instability in the short-run. Likewise, an economic crisis ensued in the United Kingdom because of COVID-19 causing spillover for the United States markets' financial instability. The examination evaluated that Asian and African nations' economic crises perilously affects the United States' financial stability. The study determined that financial instability occurred in the United States due to its own economic and health crises persisted for a longer period than financial disequilibrium that occurred in other nations. The analysis suggested some strategies of smart lockdown that the government of the United States and other nations should follow to restart the economic cycle through tighter controls to minimize losses by following the steps of (a) preparing a lockdown checklist, (b) monitoring completion of lockdown tasks, and (c) complete a close-down stock take or count.