發(fā)布時(shí)間：2018-07-16 23:06

【摘要】：本文將以物流行業(yè)的客戶主體為研究對(duì)象,著手建立區(qū)分行業(yè)的客戶授信風(fēng)險(xiǎn)等級(jí)評(píng)定指標(biāo)體系,并且嘗試應(yīng)用一種國(guó)際廣泛適應(yīng)的違約風(fēng)險(xiǎn)度量模型——KMV模型來(lái)測(cè)算預(yù)期違約率,完成對(duì)客戶授信風(fēng)險(xiǎn)等級(jí)評(píng)定結(jié)果精確性檢驗(yàn)的返回測(cè)試,達(dá)到檢驗(yàn)建立的客戶授信風(fēng)險(xiǎn)等級(jí)評(píng)定指標(biāo)體系是否有效的目的。根據(jù)股票市場(chǎng)數(shù)據(jù)度量模型的特點(diǎn),本文將KMV模型的違約風(fēng)險(xiǎn)度量方法與自身建立的客戶授信風(fēng)險(xiǎn)等級(jí)評(píng)定指標(biāo)體系的信用評(píng)級(jí)結(jié)果相結(jié)合,選取了1301家上市公司作為樣本,先采集樣本上市公司的財(cái)務(wù)報(bào)表和股票數(shù)據(jù),通過(guò)運(yùn)用自編的MATLAB程序計(jì)算出代表KMV模型的關(guān)鍵變量——違約距離和預(yù)期違約率,再對(duì)KMV模型得到的預(yù)期違約率和指標(biāo)體系等級(jí)評(píng)定結(jié)果進(jìn)行線性回歸擬合,形成一一對(duì)應(yīng)的映射關(guān)系,最后通過(guò)KMV模型計(jì)算結(jié)果對(duì)客戶授信風(fēng)險(xiǎn)等級(jí)評(píng)定指標(biāo)體系進(jìn)行樣本內(nèi)檢驗(yàn),結(jié)果顯示本文自身建立的客戶授信風(fēng)險(xiǎn)等級(jí)評(píng)定指標(biāo)體系對(duì)受評(píng)客戶違約風(fēng)險(xiǎn)判斷的準(zhǔn)確率為76.33%。通過(guò)本文的研究說(shuō)明:該指標(biāo)體系下的授信風(fēng)險(xiǎn)等級(jí)對(duì)受評(píng)客戶違約風(fēng)險(xiǎn)具有較強(qiáng)的預(yù)測(cè)能力,同時(shí)KMV模型對(duì)上市公司的違約風(fēng)險(xiǎn)考量也具有較大的參考價(jià)值。
[Abstract]:This article will take the logistics industry customer main body as the research object, starts to set up the customer credit risk grade appraisal index system which distinguishes the industry, And try to use a kind of international widely adapted default risk measurement model-KMV model to calculate the expected default rate, and complete the return test of the accuracy test of customer credit risk rating results. To verify the effectiveness of the established customer credit risk rating index system. According to the characteristics of the stock market data measurement model, this paper combines the default risk measurement method of the KMV model with the credit rating results of the customer credit risk rating index system established by itself, and selects 1301 listed companies as samples. Firstly, the financial statements and stock data of the listed companies are collected, and the key variables representing the KMV model, the default distance and the expected default rate, are calculated by using the MATLAB program. Then the expected default rate obtained by KMV model and the evaluation results of index system grade are fitted by linear regression to form a one-to-one mapping relationship. Finally, through the KMV model calculation results of customer credit risk rating index system for in-sample test, the results show that the customer credit risk rating evaluation index system established in this paper for the evaluation of customer default risk accuracy is 76.33. Through the research of this paper, it is shown that the credit risk grade under the index system has a strong ability to predict the default risk of the evaluated customer, and the KMV model is also of great reference value to the listed company's default risk evaluation.
【學(xué)位授予單位】：上海交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：F259.2

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 竇文章;劉西;;基于CreditMetrics模型評(píng)估銀行信貸的信用風(fēng)險(xiǎn)[J];改革與戰(zhàn)略;2008年10期

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本文編號(hào)：2127968