Testing performance of an interest rate commission agent banking system (AIRCABS)
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DOIhttp://dx.doi.org/10.21511/bbs.12(3).2017.09
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Article InfoVolume 12 2017, Issue #3, pp. 113-141
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This paper sought to analyze data and interpret statistical results in testing the performance of an interest rate commission agent banking system. Primary and secondary data were collected from banking industry in Ethiopia to test the research hypotheses, credit risk and liquidity crunch have no impact on AIRCABS, investor loan funding has a positive impact on profitability and sustainability of AIRCABS and discrete market deposit interest rate incentive has a positive impact on stable deposit mobilization in a bank. To test the hypothesis, statistical tools such as Cronbach’s alpha, Kuder-Richardson (KR-20), canonical correlation and multinomial logistic regression were used. The result showed that credit risk and liquidity crunch have no effect on an interest rate commission agent banking system, investor loan funding has a significant strong relationship with profitability and sustainability of AIRCABS and discrete market deposit interest rate incentive has also a significant strong relationship with stable deposit mobilization. This led to a conclusion that an interest rate commission agent banking system (AIRCABS) model is viable and reliable.
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JEL Classification (Paper profile tab)E21, E22, E40, G01, G21, G32
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References44
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Tables33
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Figures1
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- Figure 1. AIRCABS risk transfer mechanism
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- Table 1. Indicators of credit risk and liquidity crunch measures
- Table 2. Indicators of investor loan funding measures
- Table 3. Indicators of discrete market deposit interest incentive measures
- Table 4. Indicators of AIRCABS measures
- Table 5. Measures of liquidity crunch ratio
- Table 6. Measures of credit risk ratio
- Table 7. Measures of investor loan funding ratio
- Table 8. Measures of discrete market deposit interest incentive ratio
- Table 9. Measure of AICABS ratio
- Table 10. Descriptive statistics for credit risk and liquidity crunch
- Table 11. Descriptive statistics for AIRCABS
- Table 12. KMO and Bartlett’s test
- Table 13. Model summary rotationa
- Table 14. Variance accounted for investor loan funding survey instruments
- Table 15. Variance accounted for discrete market deposit survey instruments
- Table 16. Descriptive statistics for credit risk and liquidity crunch and AIRCABS
- Table 17. Linear combination for canonical correlation
- Table 18. Multivariate tests of significance
- Table 19. Dimension reduction analysis
- Table 20. KMO and Bartlett’s test
- Table 21. Redundancy index and effect of shared variance
- Table 22. Model fitting information of profitability and sustainability of AIRCABS
- Table 23. Model fitting information of stable deposit
- Table 24. Goodness-of-fit
- Table 25. Pseudo R-square
- Table 26. Likelihood ratio tests of profitability and sustainability of AIRCABS
- Table 27. Likelihood ratio tests of stable deposit
- Table 28. Parameter estimates of profitability and sustainability of AIRCABS
- Table 29. Parameter estimates of stable deposit
- Table 30. Classification
- Table 31. Percentage classification of stable deposit
- Table 32. Case processing summary (profitability and sustainability of AIRCABS)
- Table 33. Case processing summary (stable deposit)
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- Adrian, T. (2015). Discussion of Systematic Risk and the Solvency-Liquidity Nexus of Banks. International Journal of Central Banking, 11(3), 229-240.
- Allen, F., Carletti, E. (2006). Credit Risk Transfer and Contagion. Journal of Monetary Economics, 53, 89-111.
- Alpert, Mark I., Robert A. Peterson. (1972). On the Interpretation of Canonical Analysis. Journal of Marketing Research, 9, May, 187.
- Baicu, C. G., State, O. (2012). Banking Model under the Impact of the Post-Crisis Organizational Changes Aptto Confer Sustainable Financial Stability-Romanian Experience. Amfiteatru Economic, XIV(32), 436-450.
- Bayaga, A. (2010). Multinomial Logistic regression: Usage and application in risk analysis. JAQM, 5(2), 288-297.
- Bewick, V., Cheek, L., & Ball, J. (2005). Statistics review 14: Logistic regression. Critical Care (London, England), 9(1), 112-118.
- Blommestein, H. J., Keskinler, A., Lucas, C. (2011). Outlook for the Securitization Market, OECD Journal Financial Market Trends, 1, 1-18.
- Brown, C. C. (1982). On a goodness of fit test for logistic model based on score statistics. Communication in statistics: Theory and Method, 11, 1087-1105.
- Bruno, B., Bedendo, M. (2013). Credit risk transfer in U.S. commercial banks: What changed during the 2007-2009 Crisis? Journal of Banking Finance, 36, 3260-3273.
- Castro, F. G., Kellison, J. G, Boyd, S. J., Kopak, A. (2010). A Methodology for Conducting Integrative Mixed Methods Research and Data Analyses. Journal of Mixed Methods Research, 4(4), 342-360.
- Chronbach, L. J. (1951). Coefficient Alpha and The Internal Structure Of Tests. Psychometrical, 16(3), 297-334.
- Cohen J. (1992). A power primer: Quantitative methods in psychology. Psychol. Bull., 112(1), 155-159.
- Cortina, J. (1993). What is coefficient alpha: an examination of theory and applications. Journal of applied psychology, 78, 98-104.
- Creswell, J. W. (2003). Research design: Quantitative, Qualitative, and Mixed methods Approaches (2nded.). Sage Publication, London, 4-26.
- Curto, J. D., Pinto, J. C. (2009). The coefficient of variation asymptotic distribution in the case of non-iid random variables. Journal of Applied Statistics, 36(1), 21-32.
- Engel, R. J., Schutt, R. K. (2013). The Practice of Research in Social Work (3rd ed.). Sage Publication Inc., 97-104.
- Field, A. (2009). Discovering Statistics using SPSS. Sage: London.
- Gogoncea, R., Paun, Id. (2013). Pros and cons of using derivatives. Theoretical and Applied Economics, XX(9(589)), 87-102.
- Green S., Lissitz R., Mulaik S. (1977). Limitations of coefficient alpha as an index of test unidimensionlity. Educational Psychological Measurement, 37, 827-38.
- Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate Data Analysis (5th edition). Englewood Cliffs, NJ: Prentice Hall.
- Hosmer, D. W., Lemeshow, S. (2000). Applied Logistic Regression (2nd ed.). John Wiley and Sons Inc.NewYork.
- Kaiser, H. F. (1974). An index of Factorial Simplicity. Psychometrika, 23, 187-200.
- Kelley, K. (2007). Sample size planning for the coefficient of variation from the accuracy in parameter estimation approach. Behavior Research Methods, 39(4), 755-766.
- Lambert, Z., Durand, R. (1975). Some Precautions in Using Canonical Analysis. Journal of Marketing Research, 12, November, 468-475.
- Liu, J., Drane W., Liu X, Wu T. (2009). Examination of the relationships between environmental exposures to volatile organic compounds and biochemical liver tests: application of canonical correlation analysis. Environmental research, 109, 193-199.
- Lovie, P. (2005). Coefficient of Variation, Encyclopedia of Statistics in Behavioral Science. John Wiley & Sons, Ltd.
- El-Habil, M. (2012). An application of Multinomial Logistic regression Model. Pakistan journal of statistics and operation research, VIII(2), 271-291.
- Mandel, B. H., Morgan, D., Wei, C. (2012). The Role of Bank Credit Enhancements in Securitization. FRBNY Economic Policy Review, 18(2), 35-46.
- Memmel, M., Sachs, A., Stein, I. (2012). Contagion In The Interbank Market With Stochastic Loss Given Default. International journal Of Central Banking, 8(3), 177-206.
- Moise, N. M., Ilie, E. (2012). Financial Crisis Impact on Bank Deposits Guarantee. International Journal of Academic Research In Economics and Management Sciences, 1(1), 39-45.
- Prentice, R. L. (1976). A Generalization of the probit and logit methods for dose response curves. Biometrics, 32, 761-768.
- Pryseley, A., Mintiens, K., Knapen, K., Stede, Y. V. D., Molenberghs, G. (2010). Estimating Precision, Repeatability, and Reproducibility From Gaussian and Non-Gaussian Data: A Mixed Models Approach. Journal of applied statistics, 37(10), 1729-1747.
- Rattray, J., & Jones, M. C. (2007). Essential elements of questionnaire design and development. Journal of Clinical Nursing, 16, 234-243.
- Salvucci, S., Walter, E., Conley, V., Fink, S., & Saba, M. (1997). Measurement Error Studies at the national center for Education Statistics (NCES). Washington D.C: U.S. Department of Education, 115-120.
- Schouten, B., Calinescu, M., luiten, A. (2013). Optimizing Quality of Response through Adaptive Survey Design. Survey methodology, 39(1), 29-58.
- Sherry, A., Henson, R. K. (2005). Conducting and interpreting canonical correlation analysis in personality research: A user-friendly primer. Journal of Personality Assessment, 84, 37-48.
- Simon-oak, O. O., Jolaosho, O. M. (2013). Real Interest rate and Savings Mobilization in Nigeria. International Journal of Development and Economic Sustainability, 1(2), 28-40.
- Stewart, D., Love, W. (1968). A General Canonical Correlation Index. Psychological Bulletin, 70(3), 160-163.
- Tate, R. (2003). A comparison of selected empirical methods for assessing the structure of responses to test items. Applied Psychological Measurement, 27, 159-203.
- Tessema, A. T., Kruger, J. W. (2016). A Methodology To Test Viability of An Interest Rate Commission Agent Banking System (AIRCABS). International Journal Of Business Research, 16(5), 21-40.
- Thompson, B. (1991). A primer on the logic and use of canonical correlation Analysis. Measurement and Evaluation in Counselling and Development, 24, 80-95.
- Wagner, W., Marsh, I. W. (2006). Credit Risk transfer and Financial sector Stability. Journal of Financial Stability, 2, 173-193.
- Young, T., McCord, L., Crawford, P. J. (2010). Credit Default Swaps: The Good, The bad and The Ugly. Journal of Business and Economic Research, 8(4), 29-35.
- Zuckerman, A. M. (2011). Securitization Reform: A Coasean Cost Analysis. Harvard Business Law Review, 1(1), 303-317.
