Valuation of a loan-only credit default swap with negatively correlated default and prepayment intensities

In this paper, we consider pricing of a single-name and a two-reference basket loan-only credit default swap. Under a reduced-form framework, it is assumed that the default and prepayment intensity rates for the reference loan, which are negatively correlated, follow one-factor Cox-Ingersoll-Ross (CIR) and inverse CIR models, respectively. The default and prepayment probability formulas are calculated using a partial differential equation method and closed-form solutions are obtained. A numerical analysis and the parameters are also discussed.     

Development of a credit limit allocation model for banks using an integrated Fuzzy TOPSIS and linear programming

In this study, a credit risk concentration allocation model is developed for the banks to determine the credit risk concentration limits of their regional head’s. The proposed model is based on the Fuzzy TOPSIS (FTOPSIS) and Linear Programming (LP) approaches. FTOPSIS is easy to use and capable to keep tract of decision made in reaching the overall score by combining different types of criteria. LP combines the results of FTOPSIS and other constraints and objectives determined by the bank. Using FTOPSIS and LP together in the same model brings uniformity and a structure in credit risk concentration decisions of the banks. The developed model is tested with a real case banking application and satisfactory results are obtained. An application is also provided in the paper for illustrative purposes.     

基于供应链金融违约风险的保理决策收益模型仿真*

为分析供应链上、下游企业以及保理商等主体在参与应收账款保理时的最佳决策,在报童模型基础上,建立市场需求随机波动情景下供应链上游企业、下游企业和保理商期望收益模型,在模型中引入下游企业违约风险,并对模型进行数值仿真。研究结果表明,供应链上游企业和下游企业可在保理情况下分别制定最优批发价格决策和最优订货数量决策,使得双方收益均超过不保理情况下的水平。当下游企业违约风险相对较小时,采取保理可以提高供应链上下游企业各自的期望利润,同时降低批发价格,提高订货量。保理商选择的最优保理费率则随着下游企业违约风险的增加而提高。     

A credit ranking model for a parafinancial company based on the ELECTRE-III method and a multiobjective evolutionary algorithm

Credit rating is an assessment performed by lenders or financial institutions to determine a person’s creditworthiness based on the proposed terms of the loan. Frequently, these institutions use rating models to obtain estimates for the probabilities of default for their clients (companies, organizations, government, and individuals) and to assess the risk of credit portfolios. Numerous statistical and data mining methods are used to develop such models. In this paper, the potential of a multicriteria decision-aiding approach is studied. As a first step, the proposed methodology models the problem as a multicriteria evaluation process with multiple and in some cases, conflicting dimensions, which are integrated to derive sound recommendation for DMs. The second step of the methodology involves building a multicriteria outranking model based on ELECTRE III method. An evolutionary algorithm is used to exploit the outranking model. The methodology is applied to a small-scale financial institution operating in the agricultural sector. We compare loan applications based on their attributes and the credit profile of the customer or credit applicant. Our methodology offers the flexibility of combining heterogeneous information together with the preferences of decision makers (DMs), generating both relative and fixed rules for selecting the best loan applications among new and existing customers, which is an improvement over traditional methods The results reveal that outranking models are well suited to credit rating, providing good ranking results and suitable understanding on the relative importance of the evaluation criteria.