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Risk preference and efficiency in Chinese banking

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  • Zhu, Ning
  • Wu, Yanrui
  • Wang, Bing
  • Yu, Zhiqian

Abstract

This paper aims to measure banking efficiency by considering risk preferences in 49 Chinese commercial banks during the period of 2004–2012. It adopts a method which allows for endogenous classification of three risk preferences, namely the conservative, moderate and aggressive risk modes, by changing direction vectors. Banking efficiency is measured on the basis of optimal risk preference. The findings show that the moderate risk preference is the most appropriate strategy to achieve technical efficiency in the Chinese banking sector. However, the aggressive risk preference involving low risk costs, compulsory credit spreads and scale expansion played a critical role in promoting the development of Chinese banking sector, but its effect decreased rapidly. The findings also imply that the average technical efficiency scores of joint stock commercial banks and city commercial banks were higher than those of state-owned commercial banks under the optimal risk preference, and that the measured efficiency mainly shows a trend of improvement over time.

Suggested Citation

  • Zhu, Ning & Wu, Yanrui & Wang, Bing & Yu, Zhiqian, 2019. "Risk preference and efficiency in Chinese banking," China Economic Review, Elsevier, vol. 53(C), pages 324-341.
  • Handle: RePEc:eee:chieco:v:53:y:2019:i:c:p:324-341
    DOI: 10.1016/j.chieco.2018.11.001
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    5. Boussemart, Jean-Philippe & Leleu, Hervé & Shen, Zhiyang & Vardanyan, Michael & Zhu, Ning, 2019. "Decomposing banking performance into economic and credit risk efficiencies," European Journal of Operational Research, Elsevier, vol. 277(2), pages 719-726.
    6. Jianxu Liu & Mengjiao Wang & Ji Ma & Sanzidur Rahman & Songsak Sriboonchitta, 2020. "A Simultaneous Stochastic Frontier Model with Dependent Error Components and Dependent Composite Errors: An Application to Chinese Banking Industry," Mathematics, MDPI, vol. 8(2), pages 1-23, February.
    7. Bansal, Pooja & Kumar, Sunil & Mehra, Aparna & Gulati, Rachita, 2022. "Developing two dynamic Malmquist-Luenberger productivity indices: An illustrated application for assessing productivity performance of Indian banks," Omega, Elsevier, vol. 107(C).
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    10. Wang, Xiaoxiao & Liu, Haiming, 2022. "The impact of rollover restriction on stock price crash risk," Pacific-Basin Finance Journal, Elsevier, vol. 74(C).

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    More about this item

    Keywords

    Risk preference; Technical efficiency; Directional distance function; Chinese banks;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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