IDEAS home Printed from https://ideas.repec.org/a/eee/ecmode/v67y2017icp300-306.html
   My bibliography  Save this article

Modelling the effect of the geographical environment on Islamic banking performance: A panel quantile regression analysis

Author

Listed:
  • Jawadi, Fredj
  • Jawadi, Nabila
  • Idi Cheffou, Abdoulkarim
  • Ben Ameur, Hachmi
  • Louhichi, Wael

Abstract

While studies have focused on Islamic banking, research on the effect of the geographical environment on Islamic banks is scarce. We investigate this issue by using daily data on 12 Islamic banks in four regions (Africa, Asia, Europe, and the United States) from July 2007 to April 2016. We apply different methodological approaches (principal component analysis, panel data tests, and quantile regression). First, the principal component analysis shows that the performance of Islamic banks varies among regions. Second, the linear panel regression highlights that the geographical environment positively and significantly affects Islamic banking, suggesting the importance of externality effects. Finally, the environmental effect seems to vary with quantiles (positive effect for the lowest quantile versus negative effect for the highest quantile). This quantile specification points to nonlinearity in the environment–Islamic bank performance relationship, reflecting a time-varying discipline imposed by the Sharia board (Islamic Law). This finding helps better explain the main difference between Islamic banks in the East (Africa and Asia) and those in the West (Europe and the United States) and also enables investors to adjust their portfolio choices when considering the products of Islamic banks according to regional specificities.

Suggested Citation

  • Jawadi, Fredj & Jawadi, Nabila & Idi Cheffou, Abdoulkarim & Ben Ameur, Hachmi & Louhichi, Wael, 2017. "Modelling the effect of the geographical environment on Islamic banking performance: A panel quantile regression analysis," Economic Modelling, Elsevier, vol. 67(C), pages 300-306.
    • Handle: RePEc:eee:ecmode:v:67:y:2017:i:c:p:300-306
    DOI: 10.1016/j.econmod.2017.01.018
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0264999317301694
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econmod.2017.01.018?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Koenker, Roger & Xiao, Zhijie, 2006. "Quantile Autoregression," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 980-990, September.
    2. M. E. Arouri & H. Ben Ameur & N. Jawadi & F. Jawadi & W. Louhichi, 2013. "Are Islamic finance innovations enough for investors to escape from a financial downturn? Further evidence from portfolio simulations," Applied Economics, Taylor & Francis Journals, vol. 45(24), pages 3412-3420, August.
    3. Patrick A. Imam & Mr. Kangni R Kpodar, 2010. "Islamic Banking: How Has it Diffused?," IMF Working Papers 2010/195, International Monetary Fund.
    4. Fakhfekh, Mohamed & Hachicha, Nejib & Jawadi, Fredj & Selmi, Nadhem & Idi Cheffou, Abdoulkarim, 2016. "Measuring volatility persistence for conventional and Islamic banks: An FI-EGARCH approach," Emerging Markets Review, Elsevier, vol. 27(C), pages 84-99.
    5. Ivan A. Canay, 2011. "A simple approach to quantile regression for panel data," Econometrics Journal, Royal Economic Society, vol. 14(3), pages 368-386, October.
    6. Fredj Jawadi & Nabila Jawadi & Hachmi Ben Ameur & Abdoulkarim Idi Cheffou, 2017. "Does Islamic banking performance vary across regions? A new puzzle," Applied Economics Letters, Taylor & Francis Journals, vol. 24(8), pages 567-570, May.
    7. Fredj Jawadi & Ricardo M. Sousa, 2014. "The Relationship between Consumption and Wealth: A Quantile Regression Approach," Revue d'économie politique, Dalloz, vol. 124(4), pages 639-652.
    8. Kee H. Chung & Stephen W. Pruitt, 1994. "A Simple Approximation of Tobin's q," Financial Management, Financial Management Association, vol. 23(3), Fall.
    9. Hayashi, Fumio, 1982. "Tobin's Marginal q and Average q: A Neoclassical Interpretation," Econometrica, Econometric Society, vol. 50(1), pages 213-224, January.
    10. Lindenberg, Eric B & Ross, Stephen A, 1981. "Tobin's q Ratio and Industrial Organization," The Journal of Business, University of Chicago Press, vol. 54(1), pages 1-32, January.
    11. Gheeraert, Laurent & Weill, Laurent, 2015. "Does Islamic banking development favor macroeconomic efficiency? Evidence on the Islamic finance-growth nexus," Economic Modelling, Elsevier, vol. 47(C), pages 32-39.
    12. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    13. Fredj Jawadi & Abdoulkarim Idi Cheffou & Nabila Jawadi, 2016. "Can the Islamic bank be an emerging leader? A panel data causality analysis," Applied Economics Letters, Taylor & Francis Journals, vol. 23(14), pages 991-994, September.
    14. Fredj Jawadi & Abdoulkarim Idi Cheffou & Nabila Jawadi, 2016. "Do Islamic and Conventional Banks Really Differ? A Panel Data Statistical Analysis," Open Economies Review, Springer, vol. 27(2), pages 293-302, April.
    15. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
    16. Skinner, Douglas J., 1993. "The investment opportunity set and accounting procedure choice : Preliminary evidence," Journal of Accounting and Economics, Elsevier, vol. 16(4), pages 407-445, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shabir, Mohsin & Jiang, Ping & Hashmi, Shujahat Haider & Bakhsh, Satar, 2022. "Non-linear nexus between economic policy uncertainty and bank lending," International Review of Economics & Finance, Elsevier, vol. 79(C), pages 657-679.
    2. Ghlamallah, Ezzedine & Alexakis, Christos & Dowling, Michael & Piepenbrink, Anke, 2021. "The topics of Islamic economics and finance research," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 145-160.
    3. Serhat Yuksel & Hasan Dincer & Senol Emir, 2017. "Comparing the performance of Turkish deposit banks by using DEMATEL, Grey Relational Analysis (GRA) and MOORA approaches," World Journal of Applied Economics, WERI-World Economic Research Institute, vol. 3(2), pages 26-47, December.
    4. Khan, Abdullah & Rizvi, Syed Aun R. & Ali, Mohsin & Haroon, Omair, 2021. "A survey of Islamic finance research – Influences and influencers," Pacific-Basin Finance Journal, Elsevier, vol. 69(C).
    5. Bitar, Mohammad & Kabir Hassan, M. & Hippler, William J., 2018. "The determinants of Islamic bank capital decisions," Emerging Markets Review, Elsevier, vol. 35(C), pages 48-68.
    6. Alexakis, Christos & Izzeldin, Marwan & Johnes, Jill & Pappas, Vasileios, 2019. "Performance and productivity in Islamic and conventional banks: Evidence from the global financial crisis," Economic Modelling, Elsevier, vol. 79(C), pages 1-14.
    7. Shabir, Mohsin & Jiang, Ping & Bakhsh, Satar & Zhao, Zhongxiu, 2021. "Economic policy uncertainty and bank stability: Threshold effect of institutional quality and competition," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andini, Corrado & Andini, Monica, 2015. "A Note on Unemployment Persistence and Quantile Parameter Heterogeneity," IZA Discussion Papers 8819, Institute of Labor Economics (IZA).
    2. Abhinava Tripathi, 2021. "The Arrival of Information and Price Adjustment Across Extreme Quantiles: Global Evidence," IIM Kozhikode Society & Management Review, , vol. 10(1), pages 7-19, January.
    3. Agbeyegbe, Terence D., 2015. "An inverted U-shaped crude oil price return-implied volatility relationship," Review of Financial Economics, Elsevier, vol. 27(C), pages 28-45.
    4. Chowdhury, Biplob & Jeyasreedharan, Nagaratnam & Dungey, Mardi, 2018. "Quantile relationships between standard, diffusion and jump betas across Japanese banks," Journal of Asian Economics, Elsevier, vol. 59(C), pages 29-47.
    5. Jamal Bouoiyour & Amal Miftah & Refk Selmi, 2019. "The economic contribution of immigration on Europe: Fresh evidence from a “hybrid” quantile regression model," Working Papers hal-02346700, HAL.
    6. Montresor, Sandro & Vezzani, Antonio, 2015. "The production function of top R&D investors: Accounting for size and sector heterogeneity with quantile estimations," Research Policy, Elsevier, vol. 44(2), pages 381-393.
    7. Demian Pouzo, 2015. "On the Non-Asymptotic Properties of Regularized M-estimators," Papers 1512.06290, arXiv.org, revised Oct 2016.
    8. Panagiotidis, Theodore & Printzis, Panagiotis, 2021. "Investment and uncertainty: Are large firms different from small ones?," Journal of Economic Behavior & Organization, Elsevier, vol. 184(C), pages 302-317.
    9. Chen, Zhao & Cheng, Vivian Xinyi & Liu, Xu, 2024. "Reprint: Hypothesis testing on high dimensional quantile regression," Journal of Econometrics, Elsevier, vol. 239(2).
    10. Cho, Jin Seo & Kim, Tae-hwan & Shin, Yongcheol, 2015. "Quantile cointegration in the autoregressive distributed-lag modeling framework," Journal of Econometrics, Elsevier, vol. 188(1), pages 281-300.
    11. Marta Simões & João Sousa Andrade & Adelaide Duarte, 2012. "Convergence and Growth: Portugal in the EU 1986-2010," GEMF Working Papers 2012-13, GEMF, Faculty of Economics, University of Coimbra.
    12. You, Wanhai & Guo, Yawei & Zhu, Huiming & Tang, Yong, 2017. "Oil price shocks, economic policy uncertainty and industry stock returns in China: Asymmetric effects with quantile regression," Energy Economics, Elsevier, vol. 68(C), pages 1-18.
    13. Maria Marino & Alessio Farcomeni, 2015. "Linear quantile regression models for longitudinal experiments: an overview," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 229-247, August.
    14. Jan Fidrmuc & Jarko Fidrmuc, 2009. "Foreign Languages and Trade," CEDI Discussion Paper Series 09-03, Centre for Economic Development and Institutions(CEDI), Brunel University.
    15. Mohamed Ali Azouzi & Anis Jarboui, 2014. "CEO Emotional Intelligence and Firms’ Financial Policies. Bayesian Network Method," Contemporary Economics, University of Economics and Human Sciences in Warsaw., vol. 8(1), March.
    16. Maciejowska, Katarzyna, 2020. "Assessing the impact of renewable energy sources on the electricity price level and variability – A quantile regression approach," Energy Economics, Elsevier, vol. 85(C).
    17. Andrew Phiri, 2018. "Inflation persistence in BRICS countries: A quantile autoregressive (QAR) approach," Business and Economic Horizons (BEH), Prague Development Center, vol. 14(1), pages 97-104, January.
    18. Fredj Jawadi & Abdoulkarim Idi Cheffou & Nabila Jawadi & Wael Louhichi, 2016. "On the Reputation of Islamic Banks: a Panel Data Qualitative Econometrics Analysis," Open Economies Review, Springer, vol. 27(5), pages 987-998, November.
    19. Chung, Richard & Firth, Michael & Kim, Jeong-Bon, 2005. "Earnings management, surplus free cash flow, and external monitoring," Journal of Business Research, Elsevier, vol. 58(6), pages 766-776, June.
    20. Libo Yin & Jing Nie & Liyan Han, 2021. "Intermediary capital risk and commodity futures volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(5), pages 577-640, May.

    More about this item

    Keywords

    Islamic banking; Geographical environment; Panel data; Quantile regression; Nonlinearity;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecmode:v:67:y:2017:i:c:p:300-306. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/30411 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.