A Nonlinear Credit Rating Optimization Methodology for Resolving the Mismatch Between Credit Ratings and Loss Given Default

EasyChair Preprint 7983

Abstract

This paper investigates the mismatch phenomenon that the loss given default (LGD) caused by borrowers with high credit rating is not low. To address this problem, we develop a nonlinear credit rating optimization methodology that the credit rating increases with the decreasing LGD. It forces the LGD strictly decreasing according to the credit rating from C rating to AAA rating, which avoids the unreasonable phenomena as higher rating with higher LGD. Furthermore, the method is validated using three actual microfinance data samples from Chinese commercial banks. The empirical results show that the proposed method indeed guides the way to solve the mismatch issue between credit ratings and LGDs. Moreover, the results derived from this paper provide valuable information for the bankers, for the society, and for the bond investors to manage credit risk.

Keyphrases: Loss Given Default, credit rating, credit risk, microfinance loan

@booklet{EasyChair:7983,
  author    = {Baofeng Shi},
  title     = {A Nonlinear Credit Rating Optimization Methodology for Resolving the Mismatch Between Credit Ratings and Loss Given Default},
  howpublished = {EasyChair Preprint 7983},
  year      = {EasyChair, 2022}}