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Shadow Prices of Non-performing Loans for Chinese Banks in the Post-Crisis Era

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  • Shirong Zhao

Abstract

This paper examines how non-performing loans (NPLs) affect Chinese commercial banks before, during, and after the 2008 global financial crisis as well as the subsequent 2008--2010 stimulus. By accounting for NPLs as undesirable outputs, banks' technical efficiency is estimated using directional output distance function. The envelop theorem is applied to calculate the shadow price of NPLs. The shadow price of NPLs is the opportunity cost of reducing NPLs by one Chinese yuan. Empirical results show that the four major state-owned banks are the least technically efficient while foreign banks are the most efficient over the sample period 2007-2014. I also find that the crisis has a negative effect on banks' technical efficiency while the stimulus initially has a positive effect on four major state-owned commercial banks and joint-stock commercial banks, but later shows a negative effect with a higher default ratio and lower efficiency. Finally, the data show that the stimulus has greatly increased the shadow price of NPLs for four major state-owned commercial banks. Starting in 2011, the shadow prices of NPLs for four major state-owned commercial banks are much higher than all other bank types. JEL classification numbers: G21, L11, C13

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  • Shirong Zhao, 2020. "Shadow Prices of Non-performing Loans for Chinese Banks in the Post-Crisis Era," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 10(6), pages 1-8.
  • Handle: RePEc:spt:apfiba:v:10:y:2020:i:6:f:10_6_8
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    References listed on IDEAS

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    1. Mamatzakis, Emmanuel & Matousek, Roman & Vu, Anh Nguyet, 2016. "What is the impact of bankrupt and restructured loans on Japanese bank efficiency?," Journal of Banking & Finance, Elsevier, vol. 72(S), pages 187-202.
    2. Paolo Guarda & Abdelaziz Rouabah & Michael Vardanyan, 2013. "Identifying bank outputs and inputs with a directional technology distance function," Journal of Productivity Analysis, Springer, vol. 40(2), pages 185-195, October.
    3. George Assaf, A. & Matousek, Roman & Tsionas, Efthymios G., 2013. "Turkish bank efficiency: Bayesian estimation with undesirable outputs," Journal of Banking & Finance, Elsevier, vol. 37(2), pages 506-517.
    4. Fadzlan Sufian & Muzafar Shah Habibullah, 2010. "Developments in the efficiency of the Thailand banking sector: a DEA approach," International Journal of Development Issues, Emerald Group Publishing Limited, vol. 9(3), pages 226-245, September.
    5. Ha Vu & Sean Turnell, 2011. "Cost and Profit Efficiencies of Australian Banks and the Impact of the Global Financial Crisis," The Economic Record, The Economic Society of Australia, vol. 87(279), pages 525-536, December.
    6. Matousek, Roman & Rughoo, Aarti & Sarantis, Nicholas & George Assaf, A., 2015. "Bank performance and convergence during the financial crisis: Evidence from the ‘old’ European Union and Eurozone," Journal of Banking & Finance, Elsevier, vol. 52(C), pages 208-216.
    7. Berger, Allen N. & Hasan, Iftekhar & Zhou, Mingming, 2009. "Bank ownership and efficiency in China: What will happen in the world's largest nation?," Journal of Banking & Finance, Elsevier, vol. 33(1), pages 113-130, January.
    8. Gale Boyd & George Tolley & Joseph Pang, 2002. "Plant Level Productivity, Efficiency, and Environmental Performance of the Container Glass Industry," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 23(1), pages 29-43, September.
    9. Ameni Tarchouna & Bilel Jarraya & Abdelfettah Bouri, 2019. "Shadow prices of non-performing loans and the global financial crisis," Journal of Risk Finance, Emerald Group Publishing Limited, vol. 20(5), pages 411-434, October.
    10. Partovi, Elmira & Matousek, Roman, 2019. "Bank efficiency and non-performing loans: Evidence from Turkey," Research in International Business and Finance, Elsevier, vol. 48(C), pages 287-309.
    11. Maryam Hasannasab & Dimitris Margaritis & Christos Staikouras, 2019. "The financial crisis and the shadow price of bank capital," Annals of Operations Research, Springer, vol. 282(1), pages 131-154, November.
    12. Fare, Rolf & Grosskopf, Shawna & Noh, Dong-Woon & Weber, William, 2005. "Characteristics of a polluting technology: theory and practice," Journal of Econometrics, Elsevier, vol. 126(2), pages 469-492, June.
    13. Zhou, P. & Zhou, X. & Fan, L.W., 2014. "On estimating shadow prices of undesirable outputs with efficiency models: A literature review," Applied Energy, Elsevier, vol. 130(C), pages 799-806.
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    Cited by:

    1. Zhou, Mingquan & Yang, Yang, 2022. "Shadow price of equity and political connectedness: A study of Chinese commercial banks," International Review of Financial Analysis, Elsevier, vol. 83(C).

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    More about this item

    Keywords

    the directional output distance function; shadow price of non-performing loans; technical efficiency; Chinese banks.;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • L11 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Production, Pricing, and Market Structure; Size Distribution of Firms
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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