Structural estimation of counterparty credit risk under recovery risk

Castellano, Rosella; Corallo, Vincenzo

doi:10.1016/j.jbankfin.2022.106512

Cited by 1 publication

(1 citation statement)

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“…With the gradual increase of oil prices in the international market recently, the capital demand of petrochemical industry enterprises is increasing day by day, which is a field with huge demand for supply chain finance [ 29 ]. To help the development of the petrochemical industry, Industrial Bank issued a total of 1.989 billion yuan of loans to 115 upstream enterprises of HENG LI Petrochemical.…”

Section: Case Simulation and Model Evaluationmentioning

confidence: 99%

Assessment of associated credit risk in the supply chain based on trade credit risk contagion

et al. 2023

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Assessment of associated credit risk in the supply chain is a challenge in current credit risk management practices. This paper proposes a new approach for assessing associated credit risk in the supply chain based on graph theory and fuzzy preference theory. First, we classified the credit risk of firms in the supply chain into two types, namely firms’ “own credit risk” and “credit risk contagion”; second, we designed a system of indicators for assessing the credit risks of firms in the supply chain and used fuzzy preference relations to obtain the fuzzy comparison judgment matrix of credit risk assessment indicators, on which basis we constructed the basic model for assessing the own credit risk of firms in the supply chain; third, we established a derivative model for assessing credit risk contagion. On this basis, we carried out a comprehensive assessment of the credit risk of firms in the supply chain by combining the two assessment results, revealing the contagion effect of associated credit risk in the supply chain based on trade credit risk contagion (TCRC). The case study shows that the credit risk assessment method proposed in this paper enables banks to accurately identify the credit risk status of firms in the supply chain, which helps curb the accumulation and outbreak of systemic financial risks.

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Section: Case Simulation and Model Evaluationmentioning

confidence: 99%