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Modeling of recovery rate for a given default by non-parametric method

Author

Listed:
  • Chen, Rongda
  • Zhou, Hanxian
  • Jin, Chenglu
  • Zheng, Wei

Abstract

This paper develops a new non-parametric method in modeling recovery rates for a given default in credit risk management. Two main theoretical contributions are made to the literature. The first is the usage of an iteration procedure to get the proper bandwidth of kernels, and the second is the application of an asymmetric boundary kernel to avoid the boundary bias problem associated with symmetric kernels. Empirically, considering that the Internet finance may lead a gradual decrease in the guarantee of credit risks, this paper specifically quantifies credit risk in the Pacific-Basin area where Internet finance is rapidly developing. Moreover, a global sample of recovery rates of corporate bonds and bank loans in five different classifications are also used to check the robustness of our method. Consistent evidence is found that the non-parametric boundary kernel method proposed in this paper outperforms beta distribution method, according to the goodness-of-fit and Bootstrap tests. Our results have important implications for credit risk management.

Suggested Citation

  • Chen, Rongda & Zhou, Hanxian & Jin, Chenglu & Zheng, Wei, 2019. "Modeling of recovery rate for a given default by non-parametric method," Pacific-Basin Finance Journal, Elsevier, vol. 57(C).
    • Handle: RePEc:eee:pacfin:v:57:y:2019:i:c:s0927538x18300507
    DOI: 10.1016/j.pacfin.2018.10.014
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    References listed on IDEAS

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    Cited by:

    1. Li, Aimin & Li, Zhiyong & Bellotti, Anthony, 2023. "Predicting loss given default of unsecured consumer loans with time-varying survival scores," Pacific-Basin Finance Journal, Elsevier, vol. 78(C).
    2. Tao, Wang & Guang-shun, He & Jing, Guo & Yue, Yin & Lin-lin, Li, 2020. "Energy consumption and economic growth in China’s marine economic zones-an estimation based on partial linear model," Energy, Elsevier, vol. 205(C).
    3. Chen, Rongda & Chen, Xinhao & Jin, Chenglu & Chen, Yiyang & Chen, Jiayi, 2020. "Credit rating of online lending borrowers using recovery rates," International Review of Economics & Finance, Elsevier, vol. 68(C), pages 204-216.
    4. Frank Ranganai Matenda & Mabutho Sibanda & Eriyoti Chikodza & Victor Gumbo, 2022. "Corporate Loan Recovery Rates under Downturn Conditions in a Developing Economy: Evidence from Zimbabwe," Risks, MDPI, vol. 10(10), pages 1-24, October.

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