|
on Islamic Finance |
|
Issue of 2019‒09‒02
three papers chosen by |
| By: | Mahmood, Nihal; Masih, Mansur |
| Abstract: | This paper is an humble initial attempt at studying the effects of Islamic Banking performance on CO2 Emissions among OIC countries. Recently, there has been increasing awareness surrounding Sustainability Development Goals (SDG), which is what inspired this study. While SDG data is quite limited, there is a substantial record of CO2 Emissions, which is one of the components in calculating the SDG index. While extensive research has been done on environmental performance and firm profitability in the conventional space, there are limited studies on this area in the Islamic Finance space. The core issue that will be investigated in this paper is to assess if Islamic Banking performance is impacted or influenced by CO2 emissions. Islamic Bank Performance will be measured using aggregate Return on Equity (ROE), and Return on Asset (ROA) figures for all banks in the OIC region. This study employs GMM Panel Technique given the dynamic nature of the data. The main contribution of this paper is it is among the first attempts at examining this unique area. Islamic Finance is not only about Shariah compliant product structures, but also its overall impact on society itself (CO2 emissions serves as a measure of this). The key conclusion is that there is a correlation between Islamic Bank performance and CO2 emissions. However, in some cases the correlation was found to be positive (when examining ROA) and in others negative (when examining ROE). Policy makers need to study the trends in order to provide guidelines that would motivate the Islamic Banking industry to reduce emissions. |
| Keywords: | islamic banking performance, CO2 emissions, GMM |
| JEL: | C58 G21 Q57 |
| Date: | 2018–12–15 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:95652&r=all |
| By: | Imed Medhioub (Department of Finance and Investment, College of Economics and Administrative Sciences, Imam Muhammad Ibn Saud Islamic University); Mustapha Chaffai (Department of Management, High Business School, Sfax University) |
| Abstract: | This study examines herding behavior in four sectors of the Gulf Islamic stock markets. Based on the methodology of Chiang and Zheng (2010) and using daily prices for the GCC Islamic sectors from September 2013 to October 2018, results showed evidence of herding among investors in banking, insurance, hotels, restaurants, and foods sectors for the GCC Islamic stock market during the falling period when we consider a quantile regression analysis. In addition, we found that conventional return dispersions have a dominant influence on the Islamic GCC stock market during both falling and rising market periods in all sectors. We also found evidence of herd around the conventional sectors during down market period only in banking, hotel, restaurant, and food sectors. There is evidence of herd around the conventional sector during up market period for insurance and industrial sectors. |
| Date: | 2019–08–21 |
| URL: | http://d.repec.org/n?u=RePEc:erg:wpaper:1324&r=all |
| By: | Manel Hamdi (International Financial Group-Tunisia, Faculty of Economics and Management of Tunis, University of Tunis); Walid Chkili (International Financial Group-Tunisia, Faculty of Economics and Management of Tunis, University of Tunis) |
| Abstract: | The aim of this paper is to study the volatility and forecast accuracy of the Islamic stock market. For this purpose, we construct a new hybrid GARCH-type models based on artificial neural network (ANN). This model is applied to daily prices for DW Islamic markets during the period June 1999-December 2016. Our in-sample results show that volatility of Islamic stock market can be better described by the FIAPARCH approach that take into account asymmetry and long memory features. Considering the out of sample analysis, we have applied a hybrid forecasting model, which combines the FIAPARCH approach and the artificial neural network (ANN). Empirical results show that the proposed hybrid model (FIAPARCH-ANN) outperforms all other single models such as GARCH, FIGARCH, FIAPARCH in terms of all performance criteria used in our study. |
| Date: | 2019–08–21 |
| URL: | http://d.repec.org/n?u=RePEc:erg:wpaper:13&r=all |