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POT model for operational risk: Experience with the analysis of the data collected from Chinese commercial banks

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  • Han, Jinmian
  • Wang, Wei
  • Wang, Jiaqi

Abstract

This paper takes 533 operational risk loss events publicly announced by Chinese commercial banks in the period of 1995-2012 as the sample, using Peaks over Threshold (POT) model to quantify the operational risk. The statistical data classification indicates the internal fraud is the main type of operational risk in Chinese commercial banks. This paper explains its causes from the perspective of behavioral finance. The results are as follows: first, Chinese commercial banks' operational risk loss events show an upward trend, then downward trend beginning in 2003 and currently an upward trend again; second, through the empirical analysis, this paper simulates the extreme value distribution function, finds the optimal threshold, and calculates the VaR and ES of the operational risk of Chinese commercial banks and compare them at different confidence levels; and third, in view of behavioral finance theory, overconfidence and loss aversion contribute to high internal fraud incidence.

Suggested Citation

  • Han, Jinmian & Wang, Wei & Wang, Jiaqi, 2015. "POT model for operational risk: Experience with the analysis of the data collected from Chinese commercial banks," China Economic Review, Elsevier, vol. 36(C), pages 325-340.
    • Handle: RePEc:eee:chieco:v:36:y:2015:i:c:p:325-340
    DOI: 10.1016/j.chieco.2015.07.003
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    References listed on IDEAS

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    1. Huisman, Ronald, et al, 2001. "Tail-Index Estimates in Small Samples," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(2), pages 208-216, April.
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    Cited by:

    1. Yinhong Yao & Jianping Li, 2022. "Operational risk assessment of third-party payment platforms: a case study of China," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-20, December.
    2. Yuan Hong & Shaojian Qu, 2024. "Beyond Boundaries: The AHP-DEA Model for Holistic Cross-Banking Operational Risk Assessment," Mathematics, MDPI, vol. 12(7), pages 1-18, March.
    3. Xiaoqian Zhu & Jianping Li & Dengsheng Wu, 2019. "Should the Advanced Measurement Approach for Operational Risk be Discarded? Evidence from the Chinese Banking Industry," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 22(01), pages 1-15, March.
    4. Sinemis Zengin & Serhat Yuksel, 2016. "A Comparison of the Views of Internal Controllers/Auditors and Branch/Call Center Personnel of the Banks for Operational Risk: A Case for Turkish Banking Sector," International Journal of Finance & Banking Studies, Center for the Strategic Studies in Business and Finance, vol. 5(4), pages 10-29, July.
    5. Xu, Chi & Zheng, Chunling & Wang, Donghua & Ji, Jingru & Wang, Nuan, 2019. "Double correlation model for operational risk: Evidence from Chinese commercial banks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 327-339.

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    More about this item

    Keywords

    Operational risk; POT model; Chinese commercial banks; Behavioral finance;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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