No abstract

The latest decades have been marked by rapid climate change and global warming due to the release of greenhouse gas emissions into the atmosphere. Environmental taxes have emerged as a cost-effective way to tackle environmental degradation. However, the effectiveness of environmental taxes in reducing pollution remains a topic of ongoing debate. The purpose of this paper is to examine empirically the effects of various environmental tax categories (energy, pollution, resource and transport) on CO2 emissions in 34 OECD countries between 1995 and 2019. The dynamic panel threshold regression developed by Seo and Shin (2016) is implemented to assess whether the impact of environmental taxes on CO2 emissions depends on a given threshold level. The locally weighted scatterplot smoothing analysis provides evidence for a nonlinear association between environmental taxes and CO2 emissions. The analysis indicates the existence of one significant threshold and two regimes (lower and upper) for all environmental tax categories. The dynamic panel threshold regression reveals that the total environmental tax, energy tax and pollution tax reduce CO2 emissions in the upper regime, i.e., once a given threshold level is reached. The threshold levels are 3.002% of GDP for the total environmental tax, 1.991% for the energy tax and 0.377% for the pollution tax. Furthermore, implementing taxes on resource utilization may be effective but with limited environmental effects. Based on the research results, it is recommended that countries in the OECD implement specific environmental taxes to reduce greenhouse gas emissions.

This paper tests the ability of the regulatory capital requirement to cover credit losses at default, as carried out by the economic (optimal) capital requirement in Tunisian banks. The common factor in borrowers that leads to a credit default is systematic risk. However, the sensitivity to these factors differs between borrowers. To this end, we derived two kinds of sensitivity to systematic risk: the first is recognised by the Basel Committee; the second is derived from an economic approach. Hence, we can observe the impact of sensitivity to systematic risk on capital requirements. Empirically, we studied a sample of 100 individual borrowers from a Tunisian deposit bank that had credit in January 2020. We estimated the default probability for each borrower and then simulated their systematic risk sensitivity using the Monte Carlo approach, and compared them with the regulatory risk sensitivity. Then, we tested their effects on the economic and regulatory capital requirements. The results indicate that regulatory capital overestimates economic capital. This is due to the overestimation of borrowers’ contagion in terms of default risk, as shown by the superiority of their regulatory sensitivity systematic risk compared to the simulated risk. This leads banks to devote more capital than is really necessary to reach the regulatory standard. Hence, there was an increase in capital costs and the possibility of an arbitrage opportunity.

Purpose Several studies have studied the points that distinguish Islamic banks from conventional ones. The corresponding conclusions are a bit contradictory. This paper aims to study the similarities between Islamic and conventional banks in the Gulf countries using a new approach, namely, the clustering method based on dynamic time warping (DTW) distance. Design/methodology/approach To study the similarities between Islamic and conventional banks, in Gulf Cooperation Council (GCC) countries, this study used the DTW distance. Then, a clustering based on this distance was carried out to find out which banks are the most similar. Finally, the authors have studied the factors that explain these similarities. Findings This empirical study covered 44 Islamic banks and 46 conventional banks in GCC countries during 2006–2015. The results show that Islamic and conventional banks are included in the same cluster for Qatar, Bahrain and Oman. In contrast, Islamic and conventional banks do not share the same cluster for the Kingdom of Saudi Arabia, Kuwait and the United Arab Emirates. This is because of the establishment of interest rates below discount rates. In this case, banks are incentivized to take more risks to compensate for interest losses, which increases efficiency and allocates Islamic and conventional banks to different clusters. Accordingly, there is no absolute discrimination because of the initial status between Islamic and conventional banks. However, the overall banks, either Islamic or conventional, are discriminated through the distance of the banking applied interest rate and the social discount rate. Originality/value DTW distance-based clustering is a very suitable method for emphasizing the similarities that may exist between conventional and Islamic banks. This technique has not previously been used in the literature in question.

Since Bitcoin has frequently witnessed price fluctuations and high volatility, the factors influencing its returns and volatility is an important research subject. To accomplish this goal, we applied the Gets reduction method which has a good reputation compared to other competing approaches in terms of the statistical apparatus available for a repeated search to determine the final set of determinants and the consideration of location shifts. We found that the reduced set of explanatory variables that affects Bitcoin returns is composed of Twitter-based economic uncertainty, gold return, the return of the Euro/USD exchange rate, the return of the US Nasdaq stock exchange index, market capitalization, and Bitcoin mining difficulty. In contrast, the volatility of Bitcoin is affected by only lagged terms of the ARCH effect and the volume of this cryptocurrency.