本文選題：信用風(fēng)險(xiǎn) + 判別分析模型��； 參考：《西南財(cái)經(jīng)大學(xué)》2014年碩士論文

【摘要】：商業(yè)銀行所面臨的信用風(fēng)險(xiǎn)是銀行業(yè)所不容忽視,能否有效地度量信用風(fēng)險(xiǎn)更是影響著銀行業(yè)的長(zhǎng)足發(fā)展。隨著我國(guó)資本市場(chǎng)的不斷發(fā)展,各個(gè)行業(yè)的風(fēng)險(xiǎn)不斷增加,然而正處于轉(zhuǎn)軌與新興發(fā)展階段的我國(guó)商業(yè)銀行在信用風(fēng)險(xiǎn)的管理和度量方面的研究相對(duì)落后。這就需要我國(guó)商業(yè)銀行借鑒國(guó)際上發(fā)展較為成熟的銀行的風(fēng)險(xiǎn)度量模式,根據(jù)我國(guó)銀行業(yè)的經(jīng)營(yíng)特點(diǎn)和風(fēng)險(xiǎn)環(huán)境,建立合適的信用分析度量模式。本文選擇上市公司的信用風(fēng)險(xiǎn)度量為研究方向,試圖在梳理國(guó)內(nèi)外研究成果的基礎(chǔ)上,對(duì)公司信用風(fēng)險(xiǎn)度量進(jìn)行較為系統(tǒng)的理論分析,并選取樣本進(jìn)行實(shí)證分析,對(duì)所選擇的信用風(fēng)險(xiǎn)度量模型的適用性進(jìn)行一些探索性分析。 本文從信用風(fēng)險(xiǎn)的度量方法出發(fā),詳細(xì)介紹了當(dāng)前信用風(fēng)險(xiǎn)度量中的定性和定量分析方法,并結(jié)合我國(guó)商業(yè)銀行的現(xiàn)狀,分別采用判別分析模型與Logistic模型對(duì)所選取的ST公司與非ST公司進(jìn)行分析,建立了各自的分類依據(jù),從而考察兩個(gè)模型的判別準(zhǔn)確率；接著對(duì)KMV模型的內(nèi)容和實(shí)施步驟進(jìn)行詳盡的說明,在此基礎(chǔ)上,選取了15家ST公司與配對(duì)的15家非ST公司,分別計(jì)算了樣本公司在不同違約點(diǎn)下的違約距離,對(duì)ST公司與非ST公司的違約距離進(jìn)行Wilcoxon秩和檢驗(yàn),并對(duì)本文所采用的三種模型進(jìn)行對(duì)比,考察KMV模型的信用風(fēng)險(xiǎn)識(shí)別能力和在我國(guó)上市公司風(fēng)險(xiǎn)度量中的適用性；最后對(duì)實(shí)證分析結(jié)果進(jìn)行總結(jié),指出本文存在的不足,并就當(dāng)前我國(guó)信用風(fēng)險(xiǎn)管理現(xiàn)狀提出相應(yīng)的建議。本文的內(nèi)容分為五個(gè)章節(jié)： 第一章為緒論這一部分主要介紹文章的選題背景、相關(guān)文獻(xiàn)以及本文內(nèi)容安排等。第二章為信用風(fēng)險(xiǎn)與信用風(fēng)險(xiǎn)度量方法這一章節(jié)主要對(duì)信用風(fēng)險(xiǎn)的相關(guān)概念進(jìn)行解釋,較為詳細(xì)地介紹了不同信用風(fēng)險(xiǎn)度量模型的內(nèi)容,并根據(jù)各個(gè)模型的特點(diǎn)選出本文所要采用的信用風(fēng)險(xiǎn)度量模型。 第三章為基于多元判別分析模型與Logistic模型的上市公司信用風(fēng)險(xiǎn)度量本章節(jié)通過選取合適的樣本公司和財(cái)務(wù)指標(biāo),分別采用多元判別分析模型與Logistic模型對(duì)樣本數(shù)據(jù)進(jìn)行分析和比較。 第四章為基于KMV模型的信用風(fēng)險(xiǎn)度量基于對(duì)KMV模型的詳細(xì)介紹,選取滿足一定標(biāo)準(zhǔn)的樣本公司,對(duì)模型的參數(shù)進(jìn)行設(shè)定,分別求解出上市公司資產(chǎn)價(jià)值、波動(dòng)率、違約點(diǎn)、違約距離,并檢驗(yàn)兩類公司的違約距離是否有顯著差異,最后通過ROC曲線將本文所采用的三種度量模型進(jìn)行對(duì)比。 第五章為結(jié)論和建議對(duì)本文的研究結(jié)果和不足進(jìn)行說明,并就我國(guó)信用風(fēng)險(xiǎn)的管控現(xiàn)狀提出幾點(diǎn)建議。
[Abstract]:The credit risk faced by commercial banks can not be ignored by the banking industry, and whether the credit risk can be effectively measured affects the rapid development of the banking industry. With the development of the capital market in China, the risks of various industries are increasing. However, the research on credit risk management and measurement of commercial banks in China is relatively backward in the stage of transition and emerging development. This requires our commercial banks to draw lessons from the more mature international banks risk measurement model, according to the operating characteristics and risk environment of our banking industry, to establish a suitable credit analysis measurement model. This article chooses the listed company's credit risk measurement as the research direction, tries to comb the domestic and foreign research results, carries on the relatively systematic theory analysis to the company credit risk measurement, and selects the sample to carry on the empirical analysis. The applicability of the selected credit risk measurement model is analyzed. Starting from the measurement method of credit risk, this paper introduces the qualitative and quantitative methods of credit risk measurement in detail, and combines the present situation of commercial banks in our country. The discriminant analysis model and Logistic model are used to analyze the selected St company and non-St company respectively, and the classification basis is established to investigate the discriminant accuracy of the two models. Then, the contents and implementation steps of KMV model are explained in detail. On this basis, 15 St companies and 15 matched non-St companies are selected to calculate the default distance of the sample companies under different default points. The Wilcoxon rank sum test of the default distance between St company and non-St company is carried out, and the three models used in this paper are compared to investigate the credit risk identification ability of KMV model and its applicability in the risk measurement of listed companies in China. Finally, it summarizes the results of empirical analysis, points out the shortcomings of this paper, and puts forward corresponding suggestions on the current situation of credit risk management in China. The content of this paper is divided into five chapters: the first chapter is the introduction of this part mainly introduces the background of the article, relevant literature and the content of this article. The second chapter mainly explains the related concepts of credit risk and the measurement methods of credit risk, and introduces the content of different credit risk measurement models in detail. According to the characteristics of each model, this paper chooses the credit risk measurement model. The third chapter is the credit risk measurement of listed companies based on multivariate discriminant analysis model and Logistic model. This chapter selects appropriate sample companies and financial indicators. Multivariate discriminant analysis model and Logistic model were used to analyze and compare the sample data. The fourth chapter is the credit risk measurement based on KMV model. Based on the detailed introduction of KMV model, select the sample companies that meet certain standards, set the parameters of the model, calculate the asset value, volatility, default point of listed companies, respectively. The distance of default and the difference of default distance between the two types of companies are tested. Finally, the three measurement models used in this paper are compared by ROC curve. The fifth chapter is the conclusion and the suggestion to explain the research result and the insufficiency of this paper, and puts forward some suggestions on the present situation of the credit risk control in our country.
【學(xué)位授予單位】：西南財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：F832.51;F203;F224

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