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In this paper, we analyse how the Covid‐19 pandemic changed the dynamics of the euro to dollar exchange rate. To do so, we make use of spectral non‐causality tests to uncover the determinants of the euro to dollar exchange rate, using data that cover the pre‐Covid‐19 and the actual Covid‐19 era, by considering the exchange rate movements of other currencies, the stock market index of S&P 500, and the price of oil and gold, as well as their realized volatilities. Based on our findings, the Covid‐19 pandemic has indeed significantly changed the determinants of the euro to dollar exchange rate. Also, to investigate the potential shifts in the regimes of the euro to dollar exchange rate, we formulate a Markov‐switching model with two regimes, based on the determinants that have been found in the previous step. Based on our findings, the duration of the high volatility state in the Covid‐19 era has doubled, from almost 3 to approximately 6 days, compared to the pre‐Covid‐19 era, whereas the high volatility state in the Covid‐19 era is characterized by a statistically significant higher range of volatility compared to the pre‐Covid‐19 era.

The increase in addiction during COVID-19 is a condition that emerged as an aftermath of COVID-19-related events, for instance, fear of the spread of COVID-19, self-abstention from many activities, and restrictions established by the lockdown measures. This condition includes substance addictions such as drugs and alcohol but also behavioral addictions such as gambling, gaming, pornography, and smartphone and internet misuse.

The COVID-19 pandemic has already caused important negative consequences on the tourism industry globally. The lockdown measures suspended the tourism activities, and many tourists preferred to abstain from these activities in fear of the virus infection. As a result, investors have abandoned tourism-related companies’ stocks, impacting, even more, the tourism industry. In this paper, we examine the biggest companies’ stocks related to tourism, from the fields of airlines, cruise lines, resorts, hotel groups, travel agents, and other tourism activities (such as car rentals). Using time series analysis, we test and analyze the effect of the COVID-19 pandemic on these stocks, and we derive the spillover effects through the impulse-response functions from each company to the others. Based on our findings, the tourism-related stocks were affected by COVID-19, as shown by the causality technique, and, moreover, the tourism-related companies are interconnected with each other, transmitting the shock from a specific tourism industry to the others, as shown by the impulse-response functions.

PurposeBy combining econometrics and multifractal methods, utilizing a financial framework, this paper will examine with objectivity the economic, financial and social impact of coronavirus disease 2019 (COVID-19) on society.Design/methodology/approachThrough Granger causality, the authors test the effect of the COVID-19 pandemic on excessive gaming and gambling activities, and through econometrics and multifractal methods, they combine the results to analyze a possible long-run relationship.FindingsThe COVID-19 confirmed cases Granger cause all examined stocks. Based on the co-integration technique, and the multifractal cross-correlation analysis, a long-run relationship exists between all examined stocks and COVID-19.Originality/valueThis is an empirical examination of a very important subject in the field of economics, namely, the consequences of the COVID-19-related events on the behavior of global citizens. It proposes a different and more objective approach (than the interviews and questionnaires) in the examination of this specific subject, through a financial framework, depicting the stock performance of the gaming and online gambling-related companies, and reflecting on the activity of these companies. It combines two different approaches from two different disciplines, namely econometrics and multifractal analysis, to test and describe the causal and the long-run relationship between the phenomena examined, combining the results to an overall and multidimensional view of this occurrence.

PurposeThis research paper uses a novel methodological approach to investigate the spillover effects among the key sectors of the US economy.Design/methodology/approachThe paper links the US sectors via a node theoretic scheme based on a general equilibrium framework, whereas it estimates the general equilibrium equation as a Global Vector Autoregressive process, taking into consideration the potential existence of dominant units.FindingsBased on our findings, the dominant sector in the US economy, for the period 1992–2015, is the sector of information technology, finance and communications, a fact that gives credence to the view that the US economy is a service-driven economy. In addition, the US economy seems to benefit by the increased labour mobility across knowledge-intensive sectors, thus avoiding the ‘employment trap’ which in turn enabled the US economy to overcome the financial crisis of 2007.Originality/valueFirstly, the paper models by means of a network approach which is based on a general equilibrium framework, the linkages between the US sectors while treating the sector of information, technology, communications and finance as dominant, as dictated by its degree of centrality in the network structure. Secondly, the paper offers a robustness analysis regarding both the existence and the identification of dominant sectors (nodes) in the US economy. Thirdly, the paper studies a wide period, namely 1992–2015, fully capturing the recent global recession, while acknowledging the impact of the global crisis through the introduction of the relevant exogenous dummy variables; Lastly and most importantly, it is the first study to apply the GVAR approach in a network general equilibrium framework at the sectoral level.

Purpose: In this paper, through a novel Bayesian specification, we test whether the exchange rates are affected by the current crisis caused by the Covid-19 spread. Design/Methodology/Approach: we set out a novel Bayesian vector autoregressive model and compare it in terms of forecasting ability with the existing literature's econometric models. Findings: Based on our findings, the novel Bayesian model proposed in the present paper, is better in terms of forecasting ability than the econometric models, and more importantly, it can unveil the impact of the Covid-19 spread on the exchange rates, while the econometric models failed to shed light on this relationship. Practical Implications: The Covid-19 has affected the overall economic system, in many ways, leading to its disorganization. Such an impact is highlighted by the present paper, examining the exchange rates. Originality/Value: The Bayesian framework proposed in the present paper has novel technical components and can unveil hidden effects of an exogenous variable on a system of endogenous variables, that the classical econometric approaches fail to unveil.

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