Finance Lease Pricing Based On Credit Risk Assessment Study

Financial Leasing is the product of the development of market economy, which has great adaptability by making funds and assets circulate.Nowadays, the theory has commonly received which risk bonds & loan pricing is, in essence, pricing all the risks in them, especially credit risks. It is the only way to manage credit risk and upgrate competitiveness that we must combine credit risk evaluation and rent pricing. But at present, the practice and theroretical research all have little to do with credit risk evaluation. Therefore, it has important for carrying out a research into financial leasing pricing based on the credit evaluation when China is on the way to interest rates liberazation.The main ideas and conclusions of this paper are summarized as follows:Credit risk evaluation is the precondition and bases of financial leasing. Therefore, in the second chapter, it analyzes default probability and loss given default in details. Based on the result of the second chapter, the paper considers that we should use the mothed of evaluating the whole credit risk of the enterprise in a financial leasing project. In the in the third chapter, it introduces a model of credit risk evaluation of enterprises based on fuzzy clustering analysis.In fourth chapter, the paper puts forward a financial leasing princing model based on credit transition matrix which takes JLT model as its concrete form. JLT model portrays the dynamic default procedure of an nterprise by matching the credit risk rating change process with a Markov transimiton matrix. Considering the cash flow of financial leasing, the paper builds a pricing model by making use of the method provided by Kijima which can change the credit transfer probability under the real measure into the one under the risk-neutral probability measure.In the last chapter, we demonstrate an example to verify the model mentioned in the above chapters. It is shown that all the conlusions of the examples are consistent with the pratice. Namely, it implicate that the models we developed are logical.