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Estimation of banking technology under credit uncertainty

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  • Emir Malikov

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  • Diego Restrepo-Tobón
  • Subal Kumbhakar

Abstract

Credit risk is crucial to understanding banks’ production technology and should be explicitly accounted for when modeling the latter. The banking literature has largely accounted for risk using ex-post realizations of banks’ uncertain outputs and the variables intended to capture risk. This is equivalent to estimating an ex-post realization of bank’s production technology which, however, may not reflect optimality conditions that banks seek to satisfy under uncertainty. The ex-post estimates of technology are likely to be biased and inconsistent, and one thus may call into question the reliability of the results regarding banks’ technological characteristics broadly reported in the literature. However, the extent to which these concerns are relevant for policy analysis is an empirical question. In this paper, we offer an alternative methodology to estimate banks’ production technology based on the ex-ante cost function. We model credit uncertainty explicitly by recognizing that bank managers minimize costs subject to given expected outputs and credit risk. We estimate unobservable expected outputs and associated credit risk levels from banks’ supply functions via nonparametric kernel methods. We apply this framework to estimate production technology of U.S. commercial banks during the period from 2001 to 2010 and contrast the new estimates with those based on the ex-post models widely employed in the literature. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • Emir Malikov & Diego Restrepo-Tobón & Subal Kumbhakar, 2015. "Estimation of banking technology under credit uncertainty," Empirical Economics, Springer, vol. 49(1), pages 185-211, August.
  • Handle: RePEc:spr:empeco:v:49:y:2015:i:1:p:185-211
    DOI: 10.1007/s00181-014-0849-z
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Diego Restrepo-Tobón & Subal Kumbhakar & Kai Sun, 2015. "Obelix vs. Asterix: Size of US commercial banks and its regulatory challenge," Journal of Regulatory Economics, Springer, vol. 48(2), pages 125-168, October.
    2. Sarmiento, Miguel & Galán, Jorge E., 2017. "The influence of risk-taking on bank efficiency: Evidence from Colombia," Emerging Markets Review, Elsevier, vol. 32(C), pages 52-73.
    3. repec:eee:ejores:v:270:y:2018:i:2:p:747-760 is not listed on IDEAS
    4. Galán, Jorge E. & Sarmiento, Miguel, 2014. "Heterogeneous effects of risk-taking on bank efficiency : a stochastic frontier model with random coefficients," DES - Working Papers. Statistics and Econometrics. WS ws142013, Universidad Carlos III de Madrid. Departamento de Estadística.
    5. Tsionas, Efthymios G. & Malikov, Emir & Kumbhakar, Subal C., 2018. "An internally consistent approach to the estimation of market power and cost efficiency with an application to U.S. banking," European Journal of Operational Research, Elsevier, vol. 270(2), pages 747-760.
    6. repec:wly:japmet:v:31:y:2016:i:7:p:1407-1429 is not listed on IDEAS
    7. Emir Malikov & Subal C. Kumbhakar & Mike G. Tsionas, 2016. "A Cost System Approach to the Stochastic Directional Technology Distance Function with Undesirable Outputs: The Case of us Banks in 2001–2010," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1407-1429, November.

    More about this item

    Keywords

    Ex-ante cost function; Production uncertainty; Productivity; Returns to scale; Risk; C10; D81; G21;

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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