Factors of Non-Conformity Behavior and the Emergence of a Ponzi Game in the Riba-Free (Interest-Free) Banking System of Iran
In the interest-free banking system of Iran, the savings of society are in the form of bank deposits, and banks using the Islamic contracts, allocate the resources to applicants for obtaining facilities and credit. In the meantime, the central bank, with the aim of introducing monetary policy, determines the maximum interest rate on bank deposits in terms of macroeconomic requirements. But in recent years, the country's economic constraints with the stagflation and the consequence of the institutional weaknesses of the financial market of Iran have resulted in massive disturbances in the balance sheet of the banking system, resulting in a period of mismatch maturity in the banks' assets and liabilities and the implementation of a Ponzi game. This issue caused determination of the interest rate in long-term bank deposit contracts to be associated with non-observance of the maximum rate set by the central bank. The result of this condition was in the allocation of new sources of equipment to meet past commitments towards the old depositors and, as a result, a significant part of the supply of equipment was leaked out of the facilitating cycle and credit crunch emerged. The purpose of this study is to identify the most important factors affecting the occurrence of non-confirmatory financial banking behavior using data from 19 public and private banks of Iran. For this purpose, the causes of this non-confirmatory behavior of banks have been investigated using the panel vector autoregression method (PVAR) for the period of 2007-2015. Granger's causality test results suggest that the return of parallel markets for bank deposits, non-performing loans and the high share of the ratio of facilities to banks' deposits are all a cause of the formation of non-confirmatory behavior. Also, according to the results of impulse response functions and variance decomposition, NPL and the ratio of facilities to deposits have the highest long-term effect and also have a high contribution to explaining the changes in banks' non-confirmatory behavior in determining the interest rate on deposits.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1315509Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 737
 Taghipour, A. (2009). Banks, Stock Market And Economic Growth: The Case Of Iran. Iranian Economic Review, 14(23), 19-40.
 http://www.cbi.ir/page/2235.aspx Accessed on:10/07/2017
 https://www.ft.com/content/0d4f774a-aa9e-11dd-897c-000077b07658 Accessed on:13/09/2017
 https://www.globalresearch.ca/stagflation-in-iran-why-president-rouhanis-neoliberal-economic-package-is-empty/5408239 Accessed on:10/12/2017
 Rasolyar, M. S., Babaei, J., & Yavari, S. The Effect of Sanctions on Exchange Rates through GMM Method (A Cross Country Comparison).
 Clair, R. T., & Tucker, P. (1993). Six causes of the credit crunch. Economic Review-Federal Reserve Bank of Dallas, 1.
 Baek, E. G. (2005). A disequilibrium model of the Korean credit crunch. The Journal of the Korean Economy, 6(2), 313-336.
 Mizen, P. (2008). The credit crunch of 2007-2008: a discussion of the background, market reactions, and policy responses. Federal Reserve Bank of St. Louis Review, 90(September/October 2008).
 Kano, M., Uchida, H., Udell, G. F., & Watanabe, W. (2006). Information verifiability, bank organization, bank competition and bank-borrower relationships.
 Pazarbasioglu, C., Zhou, M. J. P., Le Leslé, V., & Moore, M. (2011). Contingent capital: economic rationale and design features. International Monetary Fund.
 Cingano, F., Manaresi, M., & Sette, E. (2013). Does credit crunch investment down. Unpublished working paper. Universita'Politecnica Marche.
 Haltenhof, S., Lee, S. J., & Stebunovs, V. (2014). The credit crunch and fall in employment during the great recession. Journal of Economic Dynamics and Control, 43, 31-57.
 Syron, R. F. (1991). Are we experiencing a credit crunch?. New England Economic Review, (Jul), 3-10.
 Sala-i-Martin, X., Bilbao-Osorio, B., Di Battista, A., Drzeniek Hanouz, M., Geiger, T., & Galvan, C. (2014). The Global Competitiveness Index 2014–2015: accelerating a robust recovery to create productive jobs and support inclusive growth. The global competitiveness report, 2015, 3-52.
 http://data.worldbank.org/ Accessed on:03/10/2017
 Love, I., & Zicchino, L. (2006). Financial development and dynamic investment behavior: Evidence from panel VAR. The Quarterly Review of Economics and Finance, 46(2), 190-210.
 Canova, F., & Ciccarelli, M. (2013). Panel Vector Autoregressive Models: A Survey☆☆ The views expressed in this article are those of the authors and do not necessarily reflect those of the ECB or the Eurosystem. VAR Models in Macroeconomics–New Developments and Applications: Essays in Honor of Christopher A. Sims (Advances in Econometrics, Volume 32) Emerald Group Publishing Limited, 32, 205-246.
 Holtz-Eakin, D., Newey, W., & Rosen, H. S. (1988). Estimating vector autoregressions with panel data. Econometrica: Journal of the Econometric Society, 1371-1395.
 Neyman, J., & Scott, E. L. (1948). Consistent estimates based on partially consistent observations. Econometrica: Journal of the Econometric Society, 1-32.
 Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The review of economic studies, 58(2), 277-297.
 Ahn, S. C., & Schmidt, P. (1995). Efficient estimation of models for dynamic panel data. Journal of econometrics, 68(1), 5-27.
 Arellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of econometrics, 68(1), 29-51.
 Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of econometrics, 87(1), 115-143.
 Binder, M., Hsiao, C., & Pesaran, M. H. (2005). Estimation and inference in short panel vector autoregressions with unit roots and cointegration. Econometric Theory, 21(04), 795-837.
 Huang, B. N., Hwang, M. J., & Yang, C. W. (2008). Causal relationship between energy consumption and GDP growth revisited: a dynamic panel data approach. Ecological economics, 67(1), 41-54.
 Bond, S. R. (2002). Dynamic panel data models: a guide to micro data methods and practice. Portuguese economic journal, 1(2), 141-162.
 http://ibi.ac.ir/news/377-1395 Accessed on:11/09/2017
 http://www.tgju.org/ Accessed on:03/09/2017
 Wang, S. S., Zhou, D. Q., Zhou, P., & Wang, Q. W. (2011). CO 2 emissions, energy consumption and economic growth in China: a panel data analysis. Energy Policy, 39(9), 4870-4875.
 Lean, H.H., Smyth, R., 2010. CO2 emissions, electricity consumption and output in ASEAN. Applied Energy 87, 1858–1864.
 Maddala, G. S., & Wu, S. (1999). A comparative study of unit root tests with panel data and a new simple test. Oxford Bulletin of Economics and statistics, 61(S1), 631-652.
 Pedroni, P. (1999). Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics and statistics, 61(s 1), 653-670.
 Pedroni, P. (2004). Panel cointegration: asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis. Econometric theory, 20(03), 597-625.
 Kao, C. (1999). Spurious regression and residual-based tests for cointegration in panel data. Journal of econometrics, 90(1), 1-44.
 Andrews, D. W., & Lu, B. (2001). Consistent model and moment selection procedures for GMM estimation with application to dynamic panel data models. Journal of Econometrics, 101(1), 123-164.
 Abrigo, M. R., & Love, I. (2015). Estimation of panel vector autoregression in Stata: A package of programs. manuscript, Febr 2015 available on http://paneldataconference2015.ceu. hu/Program/Michael-Abrigo. pdf.