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

Rongda Chen, Hanxian Zhou, Chenglu Jin and Wei Zheng

Pacific-Basin Finance Journal, 2019, vol. 57, issue C

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.

Keywords: Default recovery rate; Non-parametric estimation; Kernel function; Optimal bandwidth; Boundary problem (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:pacfin:v:57:y:2019:i:c:s0927538x18300507

DOI: 10.1016/j.pacfin.2018.10.014

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