Journal of International Commerce, Economics and Policy

This paper aims to contribute to the literature on the determinants of real exchange rate volatility by investigating the effect of Aid for Trade (AfT) flows on real exchange rate volatility in recipient-countries. The empirical findings show that over the full sample, AfT flows influence negatively the volatility of real exchange rate, with a lower reducing effect on Least developed countries (LDCs) compared to NonLDCs. The channels through which this effect materializes include export product concentration, institutional and governance quality, foreign direct investment inflows and terms of trade volatility. These results show that AfT flows clearly matter for real exchange rate volatility.

The lack of comprehensive empirical narratives about the effects of income and price differentials, as well as possible distributional asymmetries on consumption in G7 countries, compelled this study by using both ARDL and Quantile ARDL models. NARDL results indicate that positive shocks in income have significant and positive effects on consumption in all countries. Moreover, evidence from the Quantile ARDL model indicates that positive and significant impacts were momentary except at the 95th quantile of consumption distributions in Canada. Furthermore, price variations negatively affected consumption in all G7 countries and across all distributions, with evidence of panic buying in Italy, the US and at the 5th quantile in Japan. Meanwhile, there is evidence of asymmetric effects from income and price variations on consumption in all G7 countries, whereas the influence of income variations on consumption is heterogeneous in Canada. Moreover, the asymmetric effects of price differentials were consistent across all the distributions in all the countries. Overall, to ensure consumption optimization and by extension, economic growth, differentiated policies to respond to income and price variations at all times are of the essence.

In this paper, we employ the input–output framework and recent global value chain (GVC) results to construct the index of foreign financial services adoption in manufacturing (FFSAM) that captures the role of foreign financial services in facilitating domestic manufacturing production. The FFSAM index shows that foreign financial services adoption by Asian economies has rapidly caught up with the Western world between 2007 and 2017. Asian economies tend to deploy more foreign financial services from high-income economies than low-income economies, and in high-technology manufacturing industries than low-technology manufacturing industries. Manufacturing industries that produce for domestic consumption and for exports tend to attract different groups of financial servicification providers. It is found that the FFSAM index is affected by the GVC position, financial institution factors, and financial market characteristics.

This paper presents how volatility propagates through the cryptocurrency market. Our paper provides evidence for volatility connectedness on cryptocurrencies. The different econometric techniques, including the stochastic volatility (SVOL) model and time-varying parameter VAR models using a quasi-Bayesian local likelihood (QBLL), are applied to measure the volatility of the cryptocurrency market. Using high-frequency, intra-day data of the largest cryptocurrencies over 2018–2021, we detect the great volatility of the cryptocurrency market are the beginning of 2019, the beginning of 2020, and throughout the year of 2021. The total connectedness values suggest that the cryptocurrency market becomes volatile as the new strains of the COVID-19 appear at the end of 2021. However, by using directional connectedness, we reveal that there are negative and positive spillovers from a specific cryptocurrency to other cryptocurrencies. The great fluctuations in the period before the COVID-19 health crisis stem from the positive resonance (symmetric) between the volatility of each cryptocurrency, while this health crisis leads to substantially positive and negative spillovers (asymmetric) of cryptocurrencies, and this makes market volatility weaker than it actually is.