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Why fully efficient banks matter? A nonparametric stochastic frontier approach in the presence of fully efficient banks

Author

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  • Kien C. Tran

    (University of Lethbridge)

  • Mike G. Tsionas

    (Lancaster University Management School)

  • Emmanuel Mamatzakis

    (University of Sussex Business School)

Abstract

A common assumption in the banking stochastic performance literature refers to the non-existence of fully efficient banks. This paper relaxes this strong assumption and proposes an alternative semiparametric zero-inefficiency stochastic frontier model. Specifically, we consider a nonparametric specification of the frontier whilst maintaining the parametric specification of the probability of fully efficient bank. We propose an iterative local maximum likelihood procedure that achieves the optimal convergence rates of both nonparametric frontier and the parameters contained in the probability of fully efficient bank. In an empirical application, we apply the proposed model and the estimation procedure to a global banking data set to derive new corrected measures of bank performance and productivity growth across the world. The results show that there is variability across regions, and the probability of fully efficient bank is mostly affected by bank-specific variables that are related to bank’s risk-taking attitude, whereas country-specific variables, such as inflation, also have an effect.

Suggested Citation

  • Kien C. Tran & Mike G. Tsionas & Emmanuel Mamatzakis, 2020. "Why fully efficient banks matter? A nonparametric stochastic frontier approach in the presence of fully efficient banks," Empirical Economics, Springer, vol. 58(6), pages 2733-2760, June.
  • Handle: RePEc:spr:empeco:v:58:y:2020:i:6:d:10.1007_s00181-018-01618-9
    DOI: 10.1007/s00181-018-01618-9
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    1. Mester, Loretta J., 1996. "A study of bank efficiency taking into account risk-preferences," Journal of Banking & Finance, Elsevier, vol. 20(6), pages 1025-1045, July.
    2. Berger, Allen N. & Mester, Loretta J., 2003. "Explaining the dramatic changes in performance of US banks: technological change, deregulation, and dynamic changes in competition," Journal of Financial Intermediation, Elsevier, vol. 12(1), pages 57-95, January.
    3. Griffin, J. E. & Steel, M. F. J., 2004. "Semiparametric Bayesian inference for stochastic frontier models," Journal of Econometrics, Elsevier, vol. 123(1), pages 121-152, November.
    4. Manthos D. Delis & Panagiotis K. Staikouras, 2011. "Supervisory Effectiveness and Bank Risk," Review of Finance, European Finance Association, vol. 15(3), pages 511-543.
    5. Kumbhakar, Subal C, et al, 2001. "The Effects of Deregulation on the Performance of Financial Institutions: The Case of Spanish Savings Banks," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 33(1), pages 101-120, February.
    6. Daniel Covitz & Nellie Liang & Gustavo A. Suarez, 2013. "The Evolution of a Financial Crisis: Collapse of the Asset-Backed Commercial Paper Market," Journal of Finance, American Finance Association, vol. 68(3), pages 815-848, June.
    7. Sailesh Tanna & Fotios Pasiouras & Matthias Nnadi, 2011. "The Effect of Board Size and Composition on the Efficiency of UK Banks," International Journal of the Economics of Business, Taylor & Francis Journals, vol. 18(3), pages 441-462, November.
    8. Franklin Allen & Elena Carletti, 2010. "An Overview of the Crisis: Causes, Consequences, and Solutions," International Review of Finance, International Review of Finance Ltd., vol. 10(1), pages 1-26, March.
    9. Feng, Guohua & Serletis, Apostolos, 2010. "Efficiency, technical change, and returns to scale in large US banks: Panel data evidence from an output distance function satisfying theoretical regularity," Journal of Banking & Finance, Elsevier, vol. 34(1), pages 127-138, January.
    10. Mian Huang & Runze Li & Shaoli Wang, 2013. "Nonparametric Mixture of Regression Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(503), pages 929-941, September.
    11. Carlos Martins-Filho & Feng Yao, 2015. "Semiparametric Stochastic Frontier Estimation via Profile Likelihood," Econometric Reviews, Taylor & Francis Journals, vol. 34(4), pages 413-451, April.
    12. Canhoto, Ana & Dermine, Jean, 2003. "A note on banking efficiency in Portugal, New vs. Old banks," Journal of Banking & Finance, Elsevier, vol. 27(11), pages 2087-2098, November.
    13. Tortosa-Ausina, Emili & Grifell-Tatje, Emili & Armero, Carmen & Conesa, David, 2008. "Sensitivity analysis of efficiency and Malmquist productivity indices: An application to Spanish savings banks," European Journal of Operational Research, Elsevier, vol. 184(3), pages 1062-1084, February.
    14. DeYoung, Robert & Hasan, Iftekhar, 1998. "The performance of de novo commercial banks: A profit efficiency approach," Journal of Banking & Finance, Elsevier, vol. 22(5), pages 565-587, May.
    15. Seunghwa Rho & Peter Schmidt, 2015. "Are all firms inefficient?," Journal of Productivity Analysis, Springer, vol. 43(3), pages 327-349, June.
    16. Barros, Carlos Pestana & Weber, William L., 2009. "Productivity growth and biased technological change in UK airports," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 45(4), pages 642-653, July.
    17. Franklin Allen & Elena Carletti, 2010. "An Overview of the Crisis: Causes, Consequences, and Solutions-super-," International Review of Finance, International Review of Finance Ltd., vol. 10(s1), pages 1-26.
    18. Feng, Guohua & Zhang, Xiaohui, 2014. "Returns to scale at large banks in the US: A random coefficient stochastic frontier approach," Journal of Banking & Finance, Elsevier, vol. 39(C), pages 135-145.
    19. Fan, Yanqin & Li, Qi & Weersink, Alfons, 1996. "Semiparametric Estimation of Stochastic Production Frontier Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(4), pages 460-468, October.
    20. Barros, Carlos Pestana & Managi, Shunsuke & Matousek, Roman, 2009. "Productivity growth and biased technological change: Credit banks in Japan," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 19(5), pages 924-936, December.
    21. Alam, Ila M Semenick, 2001. "A Nonparametric Approach for Assessing Productivity Dynamics of Large U.S. Banks," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 33(1), pages 121-139, February.
    22. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
    23. Manthos D. Delis & Philip Molyneux & Fotios Pasiouras, 2011. "Regulations and Productivity Growth in Banking: Evidence from Transition Economies," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 43(4), pages 735-764, June.
    24. Markus K. Brunnermeier, 2009. "Deciphering the Liquidity and Credit Crunch 2007-2008," Journal of Economic Perspectives, American Economic Association, vol. 23(1), pages 77-100, Winter.
    25. Koutsomanoli-Filippaki, Anastasia & Mamatzakis, Emmanuel, 2009. "Performance and Merton-type default risk of listed banks in the EU: A panel VAR approach," Journal of Banking & Finance, Elsevier, vol. 33(11), pages 2050-2061, November.
    26. Kumbhakar, Subal C. & Parmeter, Christopher F. & Tsionas, Efthymios G., 2013. "A zero inefficiency stochastic frontier model," Journal of Econometrics, Elsevier, vol. 172(1), pages 66-76.
    27. Luis Orea, 2002. "Parametric Decomposition of a Generalized Malmquist Productivity Index," Journal of Productivity Analysis, Springer, vol. 18(1), pages 5-22, July.
    28. Mian Huang & Weixin Yao, 2012. "Mixture of Regression Models With Varying Mixing Proportions: A Semiparametric Approach," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 711-724, June.
    29. Koutsomanoli-Filippaki, Anastasia & Margaritis, Dimitris & Staikouras, Christos, 2009. "Efficiency and productivity growth in the banking industry of Central and Eastern Europe," Journal of Banking & Finance, Elsevier, vol. 33(3), pages 557-567, March.
    30. George Assaf, A. & Barros, Carlos P. & Matousek, Roman, 2011. "Productivity and efficiency analysis of Shinkin banks: Evidence from bootstrap and Bayesian approaches," Journal of Banking & Finance, Elsevier, vol. 35(2), pages 331-342, February.
    31. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    32. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    33. Greene, William, 2005. "Reconsidering heterogeneity in panel data estimators of the stochastic frontier model," Journal of Econometrics, Elsevier, vol. 126(2), pages 269-303, June.
    34. Parmeter, Christopher F. & Kumbhakar, Subal C., 2014. "Efficiency Analysis: A Primer on Recent Advances," Foundations and Trends(R) in Econometrics, now publishers, vol. 7(3-4), pages 191-385, December.
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    More about this item

    Keywords

    Backfitting local maximum likelihood; Mixture models; Probability of fully efficient banks; Global banking;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • G20 - Financial Economics - - Financial Institutions and Services - - - General
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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