ABSTRACT
PurposeThe COVID-19 pandemic is known to have affected the logistics and supply chains;however, there is no adequate empirical evidence to prove in which way it has affected the relationship between the stocks related to this field with the corresponding cryptocurrencies. This paper aims to test the dynamic relationship of cryptocurrencies with supply chain and logistics stocks.Design/methodology/approachIn this paper, the author tests the causal and long-run relationship between logistics and supply chain stocks with the corresponding cryptocurrencies related to these fields, or those that are known to exhibit characteristics that can be utilized by these fields, testing also whether the COVID-19 pandemic affected this relationship. To do so, the author performs the variable-lag causality to test the causal relationship, and examines if this relationship changed due to COVID-19. The author then implements the multifractal detrended cross-correlation analysis to investigate the characteristics of a possible long-run relationship, testing also whether they changed due to COVID-19.FindingsThe results indicate that there is a positive long-run relationship between each logistics and supply chain stocks and the corresponding cryptocurrencies, before and also during COVID-19, but during COVID-19 this relationship becomes weaker, in most cases. Moreover, before COVID-19, the majority of the cases indicate a causal direction from cryptocurrencies to the stocks, while during COVID-19, the causal relationships decrease in multitude, and most cases unveil a causal direction from the stocks to cryptocurrencies.Originality/valueThe causal pattern changed during COVID-19, and the long-run relationship became weaker, showing a change in the dynamics in the relationship between logistics and supply chain stocks with cryptocurrencies.
ABSTRACT
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.
ABSTRACT
Purpose The COVID-19 pandemic is known to have affected the logistics and supply chains;however, there is no adequate empirical evidence to prove in which way it has affected the relationship between the stocks related to this field with the corresponding cryptocurrencies. This paper aims to test the dynamic relationship of cryptocurrencies with supply chain and logistics stocks. Design/methodology/approach In this paper, the author tests the causal and long-run relationship between logistics and supply chain stocks with the corresponding cryptocurrencies related to these fields, or those that are known to exhibit characteristics that can be utilized by these fields, testing also whether the COVID-19 pandemic affected this relationship. To do so, the author performs the variable-lag causality to test the causal relationship, and examines if this relationship changed due to COVID-19. The author then implements the multifractal detrended cross-correlation analysis to investigate the characteristics of a possible long-run relationship, testing also whether they changed due to COVID-19. Findings The results indicate that there is a positive long-run relationship between each logistics and supply chain stocks and the corresponding cryptocurrencies, before and also during COVID-19, but during COVID-19 this relationship becomes weaker, in most cases. Moreover, before COVID-19, the majority of the cases indicate a causal direction from cryptocurrencies to the stocks, while during COVID-19, the causal relationships decrease in multitude, and most cases unveil a causal direction from the stocks to cryptocurrencies. Originality/value The causal pattern changed during COVID-19, and the long-run relationship became weaker, showing a change in the dynamics in the relationship between logistics and supply chain stocks with cryptocurrencies.
ABSTRACT
DefinitionThe 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.
ABSTRACT
The scientific community still struggles to understand the magnitude of the worldwide infections and deaths induced by COVID-19, partly ignoring the financial consequences. In this paper, using the autoregressive fractionally integrated moving average (ARFIMA)—general autoregressive conditional heteroskedasticity (GARCH) model, we quantify and show the impact of the COVID-19 spread in Italy, utilizing data for the stock market. Using information criteria and forecasting accuracy measures, we show that the COVID-19 confirmed cases contribute with statistically significant information to the modeling of volatility, and also increase the forecasting ability of the volatility of the Italian stock market index, leading to a decrease in the mean stock index.
ABSTRACT
Abstract 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.