|Title||Application of VaR Method on the Credit Risk Management of Commercial Bank|
【Abstract】 Credit risk and bank were produced at the same time, with the promotion and innovation of the credit products of the financial market, the measurement of thebecomes more and more difficult and complicated. In order to meet the need of risk measures, the of has already been turned from qualitative analysis to quantitative analysis gradually. The models of VaR credit risk, as a kind of the comprehensive risk tolerance method that accords with future risk development direction, have got the approval and support of the worldwide main commercial bank. According to the condition of our state, this paper has tried to apply modern credit of measurement model to the commercial bank of our country in risk management.Firstly this paper has expounded the feature and concept of the credit risk of commercial bank, defined the credit products, and introduced the current situation of credit risk management of commercial bank of our country and existing problem. Secondly, it explains the theories of VaR, recommends four credit risks of measurement model which are based on VaR, then describes the suitability of these models in our country of model, and draw an conclusion that the suitability of credit risk model is superior to other three models in our country. Combination of the methods of loan risk of our country with CreditRisk+ model, which have offered a way to measure the probability of individual in breach, remedies the deficiency of CreditRisk+ which has no stipulation to the debtor’s . Finally, the paper uses CreditRisk+ model to carry on positive research of certain commercial bank of our country. The result shows: It is a feasible plan to refer to to determine Credit Risk model of individual . After improving, After improving CreditRisk+ model can carry on the measurement with lost loan on the basis of existing data of commercial bank of our country.
|Subject||credit risk, creditrisk+ model, loan risk degree, probability in breach, VAR,|
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