本文關鍵詞：商業(yè)銀行基于KMV模型對上市公司客戶信用風險度量研究　出處：《西南政法大學》2012年碩士論文　論文類型：學位論文

  更多相關文章： 商業(yè)銀行 信用風險 違約點 行業(yè)分析 KMV模型

【摘要】：防范信用風險一直是商業(yè)銀行經(jīng)營管理過程中面臨的核心問題。目前國際上“歐債危機”的惡化，美國經(jīng)濟的持續(xù)低迷以及“阿拉伯之春”引起的阿拉伯世界各國的政治動蕩等不利因素進一步加劇了世界經(jīng)濟的不確定性，這種不確定性將會惡化銀行信用的外部環(huán)境，使商業(yè)銀行業(yè)面臨的信用風險進一步加大。國內通脹引起的原材料與勞動力價格的普遍上漲，增加了企業(yè)的成本，進而擠壓了企業(yè)的利潤空間。加之國內利率的市場化以及市場流動性的短缺等諸多因素的影響加大了企業(yè)的違約風險。嚴峻的信用風險形勢對我國商業(yè)銀行信用風險的防范提出了更高的要求。 本文以商業(yè)銀行面臨的上市公司信用風險為研究對象，通過改進KMV模型違約點選取的參數(shù)，使之更適用于我國金融市場現(xiàn)狀以及使銀行更準確的對上市公司客戶的信用風險進行度量。本文對國內外有關KMV模型以及信用風險度量研究的文獻進行歸類分析的基礎上，介紹了信用風險的度量由定性分析向定量模型發(fā)展的過程，通過對比國際上最有代表性的四大信用度量模型，確定KMV模型較為適合中國的金融市場環(huán)境。鑒于KMV模型在國外應用的經(jīng)濟環(huán)境與我國現(xiàn)行經(jīng)濟狀況存在很大的差異，本文對模型違約點的選取及股權市場價值的計算進行了一定的修正，使之適合我國的金融市場現(xiàn)狀以及我國信用風險管理現(xiàn)狀。然后利用修正后的KMV模型對我國4個行業(yè)中的32家上市公司的數(shù)據(jù)進行實證研究，得出以下幾個結論： 1.通過對ST公司與非ST公司三組違約點下的違約距離均值差的比較發(fā)現(xiàn)，違約點選取的參數(shù)為0.75時，即違約點DP=流動負債+0.75*長期負債時，KMV模型預測效果最顯著。 2.文章對兩組樣本即ST上市公司與非ST上市公司的違約距離進行對比分析，發(fā)現(xiàn)非ST上市公司的違約距離要顯著的大于ST的上市公司的違約距離，說明經(jīng)過修正后的模型能夠較好的區(qū)分ST公司與非ST公司的違約風險。 3.四個行業(yè)的違約距離存在明顯差異，按違約風險由大到小依次排序為：房地產(chǎn)行業(yè)、生物制藥行業(yè)、汽車行業(yè)、電力行業(yè)。 最后結合實證結果，對四個行業(yè)整體違約風險大小及風險產(chǎn)生的原因作出分析，并為商業(yè)銀行信貸管理提出建議。 全文大致分為六個部分： 第一部分為緒論，主要闡述了論文選題的背景、意義以及文章的研究思路、研究內容、研究方法和可能的創(chuàng)新之處。 第二部分是國內外相關研究現(xiàn)狀綜述，對國內外關于信用風險度量和管理的理論研究成果進行梳理，并對優(yōu)秀文獻進行簡單評述。 第三部分是分別對幾種信用風險度量方法進行優(yōu)缺點的分析，重點對KMV模型作了詳細介紹，包括模型所依據(jù)的理論基礎，研究框架和計算步驟。通過比較分析突出了KMV模型的優(yōu)勢。 第四部分是模型的修正及實證分析。該部分首先根據(jù)中國經(jīng)濟的實際狀況對模型進行合理的修正。然后從上市公司中選取32家具有代表性的上市公司（包含ST與非ST公司）作為樣本，，運用修正后的KMV模型對樣本進行實證分析，并根據(jù)實證結果進行比較分析，分析結果表明，KMV模型能較好的識別上市公司的風險。既能夠較好的區(qū)分ST公司與非ST公司的違約風險，又能夠識別不同行業(yè)的違約風險，以此說明我國應用KMV模型的可行性。 第五部分根據(jù)實證結果分析對商業(yè)銀行信用風險管理提供對策建議。 論文最后部分是總結與展望，對全文內容進行總結概括，指出了研究的局限性，并對后續(xù)的研究工作提出展望。
[Abstract]:To prevent the credit risk has been the core issue facing the management of commercial banks. The current international debt crisis worsened, political unrest and other unfavorable factors continued downturn in the US economy and the "Arabia spring" by Arabia world further exacerbated the world economic uncertainty, this uncertainty will deteriorate bank credit in the external environment, the credit risk faced by commercial banks. To further increase domestic inflation caused by raw materials and labor costs generally rose, increasing the cost of enterprises, and then squeeze corporate profit margins. Coupled with the impact of the domestic interest rate marketization and market liquidity shortage and other factors increase the enterprise default risk. Put forward higher requirements for credit risk situation of Chinese commercial bank credit risk prevention.
The credit risk of listed companies are taking commercial banks as the research object, through the parameters of the improved KMV model default point is selected, which is more suitable for China's financial market situation and make banks more accurate to the listed company credit risk measurement. The basis of the classified analysis in the literature on relevant research at home and abroad to measure KMV model and credit risk, introduces the measurement of credit risk from qualitative analysis to quantitative model of the development process, through the international comparison of the most representative of the four major credit measure model, the KMV model is more suitable for China financial market environment. In view of the KMV model, there is a big difference in foreign economic environment and I in the current economic situation, this paper made some amendments to the model default point selection and stock market value calculation, which is suitable for China's financial market The status quo and the current situation of credit risk management in China are analyzed. Then the data of 32 listed companies in 4 industries in China are empirically studied by using the revised KMV model.
1., by comparing the distance difference between the three groups of default points of ST company and non ST company, it is found that when the default point selection parameter is 0.75, that is, the default point DP=, the +0.75* liability is the most significant.
The 2. pairs of two samples of ST and non ST listed companies default distance for comparative analysis, found that non ST listed companies default distance are significantly greater than ST distance to default of listed companies, the modified model can distinguish between ST companies and non ST companies default risk.
3., there are obvious differences between the four industries' default distance. According to the risk of default, they are ranked as follows: the real estate industry, the biopharmaceutical industry, the automotive industry, the electric power industry.
Finally, based on the empirical results, this paper makes an analysis of the size of the four industries as a whole and the causes of the risk, and puts forward some suggestions for the credit management of the commercial banks.
The full text is roughly divided into six parts:
The first part is the introduction, which mainly expounds the background, significance and research ideas, research contents, research methods and possible innovations of the thesis.
The second part is the summary of the related research at home and abroad, and the theoretical research results of credit risk measurement and management at home and abroad are reviewed, and the excellent literature is simply commented.
The third part is the analysis of the advantages and disadvantages of several credit risk measurement methods. The KMV model is introduced in detail, including the theoretical basis, research framework and calculation steps based on the model. Through comparative analysis, the advantages of KMV model are highlighted.
The fourth part is the empirical analysis and correction model. Firstly, according to the actual situation of the economic Chinese madereasonable amendment to the model. Then from the listed companies in the selection of 32 representative listed companies (including ST and non ST companies) as a sample, using the modified KMV model to analyze the samples, and according to comparative analysis of the empirical results, the analysis results show that the KMV model can better identify the risk of listed companies. It can distinguish between ST companies and non ST companies default risk, and to identify the different sectors of the risk of default, in order to show the feasibility of the application of KMV model in China.
The fifth part provides countermeasures and suggestions on the credit risk management of commercial banks according to the empirical results.
The last part of the paper is the summary and prospect, summarizes the content of the full text, points out the limitations of the research, and puts forward the prospect of the follow-up research work.

【學位授予單位】：西南政法大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：F832.33

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