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Exploring the mismatch between credit ratings and loss-given-default: A credit risk approach

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  • Shi, Baofeng
  • Chi, Guotai
  • Li, Weiping

Abstract

It is commonly observed that high grade loans with better ratings are often associated with low recoveries if they default (i.e. with relatively high loss-given-default (LGD)). To address the mismatch problem, this paper proposes a credit risk approach by minimizing LGD for higher rated loans as a risk-rating matching standard in the sense that the decreasing LGD from creditors’ perspective is associated with higher credit rating for the borrower. This standard forces customers’ credit rating of each grade to be optimally determined in correspondence to its LGD, which means the LGD of high grade loans tends to be low. The approach is then tested using three credit datasets from China, i.e. credit data from 2044 farmers, 2157 small private businesses and 3111 SMEs. The empirical results show that the proposed approach indeed guides the way to solve the mismatch phenomenon between credit ratings and LGDs in the existing credit rating literature. By optimally determining credit ratings, the findings derived from this paper help provide a valuable reference for bankers, and bond investors to manage their credit risk.

Suggested Citation

  • Shi, Baofeng & Chi, Guotai & Li, Weiping, 2020. "Exploring the mismatch between credit ratings and loss-given-default: A credit risk approach," Economic Modelling, Elsevier, vol. 85(C), pages 420-428.
  • Handle: RePEc:eee:ecmode:v:85:y:2020:i:c:p:420-428
    DOI: 10.1016/j.econmod.2019.11.032
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    Cited by:

    1. Medina-Olivares, Victor & Calabrese, Raffaella & Dong, Yizhe & Shi, Baofeng, 2022. "Spatial dependence in microfinance credit default," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1071-1085.
    2. Zhang, Xuan & Ouyang, Ruolan & Liu, Ding & Xu, Liao, 2020. "Determinants of corporate default risk in China: The role of financial constraints," Economic Modelling, Elsevier, vol. 92(C), pages 87-98.
    3. Zhang, Xuan & Zhao, Yang & Yao, Xiao, 2022. "Forecasting corporate default risk in China," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1054-1070.
    4. Sun, Yue & Chai, Nana & Dong, Yizhe & Shi, Baofeng, 2022. "Assessing and predicting small industrial enterprises’ credit ratings: A fuzzy decision-making approach," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1158-1172.
    5. 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.
    6. Nana Chai & Baofeng Shi & Bin Meng & Yizhe Dong, 2023. "Default Feature Selection in Credit Risk Modeling: Evidence From Chinese Small Enterprises," SAGE Open, , vol. 13(2), pages 21582440231, April.

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    More about this item

    Keywords

    Credit rating; Loss-given-default; Credit rating matching; Microfinance loan; China;
    All these keywords.

    JEL classification:

    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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