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Credit Risk Measurement Of The Listed Companies In China Based On KMV Model

Posted on:2013-03-11Degree:MasterType:Thesis
Country:ChinaCandidate:Y YangFull Text:PDF
GTID:2249330374481377Subject:Financial mathematics and financial engineering
Abstract/Summary:PDF Full Text Request
Credit risk is the most important risk of domestic banking sector, credit risk not only bring the economic loss to the banks, but also increase the cost of the banks’ management, credit risk has become the core content of the banks’ risk management. Facing the increasingly sophisticated and complex financial mar-kets, and international regulatory standards.Domestic banks must learn foreign advanced credit risk quantification management tools, combined with the current conditions, develop and establish an effective credit risk measurement models to strengthen credit risk management and improve the core competitiveness.On the basis of the modern credit risk management theory, firstly, this pa-per systematically expounded the scholars’research about the credit risk and the application and effectiveness of KMV model. Secondly, briefly introduced the definition of credit risk from the traditional and modern significance, as well as its own characteristics different from other financial risks and causes, be-sides, explored the traditional credit risk measurement methods, for example, the expert rating system, credit rating system and financial indicators model, in addition, introduced the modern credit risk measurement methods,for example, credit metrics model and credit portfolio view model. Thirdly, detailed the the-oretical basis and calculation method of KMV model, combined with the actual financial environment, selected BVPS as the price of non-tradable shares, deter-mined short-term liabilities plus1/2long-term liabilities as the default point. In the empirical analysis section, selected13ST companies and non-ST companies, calculated the default distance and expected default frequency, besides,tested significance of difference of the default distance, come to the conclusion that the default distance of non-ST companies is greater than ST companies, in the meantime, verify the credit risk identification capabilities.. Finally, this paper put forward some suggestions to improve credit risk management in the domestic commercial bank.
Keywords/Search Tags:Listed company, Credit risk, KMV model, Default-distance, EM algorithm
PDF Full Text Request
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