本文選題：信用風(fēng)險(xiǎn) + KMV模型�。� 參考：《江西財(cái)經(jīng)大學(xué)》2012年碩士論文

【摘要】：信用風(fēng)險(xiǎn)的度量是商業(yè)銀行經(jīng)營的一個(gè)永恒話題。近年來,隨著金融全球化和經(jīng)濟(jì)全球化進(jìn)程的加快,商業(yè)銀行面臨的信用風(fēng)險(xiǎn)越來越大,亦越來越復(fù)雜。因此,如何有效地控制和度量信用風(fēng)險(xiǎn)成為社會(huì)各界人士關(guān)注的焦點(diǎn)。由于中國經(jīng)濟(jì)體制的特殊性,目前中國商業(yè)銀行主要還是使用傳統(tǒng)的方法(如信用分析方法、專家經(jīng)驗(yàn)判斷法等)對(duì)信用風(fēng)險(xiǎn)進(jìn)行度量,這樣的度量方式遠(yuǎn)不能滿足商業(yè)銀行對(duì)企業(yè)信用風(fēng)險(xiǎn)度量的要求。因此,研究國外先進(jìn)的信用風(fēng)險(xiǎn)度量模型,開發(fā)適合中國國情的信用風(fēng)險(xiǎn)度量模型,對(duì)提高中國銀行業(yè)信用風(fēng)險(xiǎn)管理的能力具有重要意義。 本文從信用風(fēng)險(xiǎn)的界定、風(fēng)險(xiǎn)波動(dòng)性、風(fēng)險(xiǎn)驅(qū)動(dòng)因素和相關(guān)性、回收率以及適用范圍等五個(gè)方面分析比較現(xiàn)代信用風(fēng)險(xiǎn)度量方法在中國的適用性。根據(jù)目前中國商業(yè)銀行的信用風(fēng)險(xiǎn)特征和管理現(xiàn)狀,選取KMV模型,并對(duì)模型中的股權(quán)和違約點(diǎn)的確定做了一定的修正,以中國上市公司的數(shù)據(jù)為樣本,對(duì)中國商業(yè)銀行信用風(fēng)險(xiǎn)度量進(jìn)行實(shí)證分析,得到的主要結(jié)論如下： (1)利用違約距離和違約概率度量上市公司的違約情況,效果良好。由于目前中國還沒有像KMV公司一樣擁有龐大的數(shù)據(jù)庫,因此無法通過違約距離得到經(jīng)驗(yàn)EDF,但利用違約距離和違約概率度量上市公司的違約情況,也起到了很好的效果。實(shí)證中顯示,ST公司的信用風(fēng)險(xiǎn)一般都比正常公司的信用風(fēng)險(xiǎn)大。表明此方法對(duì)中國上市公司的信用風(fēng)險(xiǎn)有一定的預(yù)測(cè)能力,從而警示銀行及早作好防范決策。 (2)把違約點(diǎn)設(shè)定為“流動(dòng)負(fù)債+0.75×長(zhǎng)期負(fù)債”,得到的結(jié)果更適合中國企業(yè)的信用狀況。在KMV模型中,違約點(diǎn)的確定是該模型的主要步驟之一。KMV公司通過大量違約事件進(jìn)行驗(yàn)證,當(dāng)違約點(diǎn)為“流動(dòng)負(fù)債+0.5×長(zhǎng)期負(fù)債”時(shí),最能反映上市公司的違約情況。對(duì)于這個(gè)違約點(diǎn)的適用性是否符合中國的經(jīng)濟(jì)體制,這個(gè)值得我們考慮。因此,本文在選用KMV公司確定的違約點(diǎn)基礎(chǔ)上,增加一個(gè)違約點(diǎn),即取“流動(dòng)負(fù)債+0.75×長(zhǎng)期負(fù)債”,并分別對(duì)這兩個(gè)違約點(diǎn)計(jì)算出它們各自的違約距離和違約概率,然后通過簡(jiǎn)單的均值檢驗(yàn)以及T檢驗(yàn)方法判定這兩種違約點(diǎn)的適用性。兩種違約點(diǎn)均能通過檢驗(yàn),但是當(dāng)“違約點(diǎn)=流動(dòng)負(fù)債+0.75×長(zhǎng)期負(fù)債”時(shí),所得到的結(jié)果更適合中國基本情況。這與KMV公司所確定的違約點(diǎn)有些差異,導(dǎo)致這種差異的主要原因是由于中國上市企業(yè)信用的嚴(yán)重缺失。從客觀上來說,只有當(dāng)企業(yè)的負(fù)債總額高于企業(yè)資產(chǎn)價(jià)值的一定比例時(shí),企業(yè)才會(huì)出現(xiàn)違約現(xiàn)象。因此,KMV公司將違約點(diǎn)設(shè)在“流動(dòng)負(fù)債+0.5×長(zhǎng)期負(fù)債”是比較合適的。但由于中國上市企業(yè)的信用缺失情形比較嚴(yán)重,很多企業(yè)為避免虧損,當(dāng)其資產(chǎn)價(jià)值出現(xiàn)下滑但還未低于負(fù)債總額時(shí),就會(huì)出現(xiàn)違約情況。因此,把違約點(diǎn)設(shè)定為“流動(dòng)負(fù)債+0.75×長(zhǎng)期負(fù)債”是與中國企業(yè)信用狀況相適應(yīng)的。 加強(qiáng)中國商業(yè)銀行信用風(fēng)險(xiǎn)管理,提高中國商業(yè)銀行信用風(fēng)險(xiǎn)度量的準(zhǔn)確性、科學(xué)性,一要加強(qiáng)現(xiàn)代信用風(fēng)險(xiǎn)管理文化意識(shí)；二要建立健全的相關(guān)信用機(jī)制；三要建立信用數(shù)據(jù)庫；四要建立科學(xué)有效的信用風(fēng)險(xiǎn)度量模型,對(duì)信用風(fēng)險(xiǎn)實(shí)現(xiàn)全方位的度量。
[Abstract]:The measurement of credit risk is an eternal topic for commercial banks. In recent years, with the rapid development of financial globalization and economic globalization, the credit risk of commercial banks is becoming more and more complex. Therefore, how to effectively control and measure credit risk has become the focus of attention of all circles of society. At present, the commercial banks of China mainly use the traditional methods (such as credit analysis method, expert experience judgment method, etc.) to measure the credit risk. This measure can not meet the requirements of the commercial bank's credit risk measurement. The credit risk measurement model suitable for China's national conditions is of great significance for improving the credit risk management ability of China's banking industry.
This paper compares the applicability of the modern credit risk measurement method in China from five aspects, such as the definition of credit risk, risk volatility, risk driving factors and relevance, recovery rate and the scope of application. According to the current credit risk characteristics and management status of China's commercial banks, the KMV model is selected and the equity and violation in the model are made and violated. With the data of Chinese listed companies as samples, the empirical analysis of the credit risk measurement of Chinese commercial banks is carried out. The main conclusions are as follows:
(1) the default distance and default probability are used to measure the default situation of the listed companies, and the effect is good. Since there is no huge database like KMV company in China at present, it can not get experience EDF through default distance, but it has also played a very good effect by measuring default distance and default probability on the default situation of the company. The demonstration shows that the credit risk of ST company is generally greater than that of the normal company. It shows that this method has a certain predictive ability for the credit risk of Chinese listed companies, thus warning the bank to make early preventive decisions.
(2) the default point is set as "+0.75 * long-term liabilities of current liabilities". The results obtained are more suitable for the credit status of Chinese enterprises. In the KMV model, the determination of the default point is one of the main steps of the model..KMV company is verified by a large number of default events. When the default point is "current liabilities +0.5 x long term liabilities", it can best reflect It is worth considering whether the applicability of the city is in conformity with the economic system of China, which is worth considering. Therefore, on the basis of the default point determined by KMV company, this paper adds a default point, that is, the "current liabilities +0.75 x long liabilities", and to calculate their respective violation of the two default points respectively. About distance and default probability, and then using a simple mean test and T test to determine the applicability of the two default points. Two kinds of default points can be tested, but when the "default = +0.75 x long liabilities", the results are more suitable for China's basic situation. This is somewhat inferior to the default point identified by KMV company. The main reason for this difference is due to the serious lack of credit in China's listed companies. Objectively, only when the total amount of liabilities of the enterprise is higher than the value of the enterprise asset value, the enterprise will appear to be in breach of contract. Therefore, it is more appropriate for KMV company to set a default point in the "+0.5 x long liability" of the "current liabilities". However, due to the serious lack of credit in China's listed companies, many enterprises in order to avoid losses, when the value of assets decline but is still not lower than the total liabilities, there will be a default situation. Therefore, the default point is set as "+0.75 * long-term liabilities of current liabilities" is compatible with the credit status of Chinese enterprises.
To strengthen the credit risk management of Chinese commercial banks and to improve the accuracy and scientificity of the credit risk measurement of China's commercial banks, we should strengthen the cultural awareness of modern credit risk management; two to establish a sound related credit mechanism; three to establish a credit database; four to establish a scientific and effective credit risk measurement model, and to the credit risk. Achieve an omni-directional measure.

【學(xué)位授予單位】：江西財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：F832.33;F224

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 張玲,楊貞柿;信用風(fēng)險(xiǎn)度量方法綜述[J];財(cái)經(jīng)科學(xué);2004年S1期

2 于立勇,詹捷輝;基于Logistic回歸分析的違約概率預(yù)測(cè)研究[J];財(cái)經(jīng)研究;2004年09期

3 張玲,楊貞柿,陳收;KMV模型在上市公司信用風(fēng)險(xiǎn)評(píng)價(jià)中的應(yīng)用研究[J];系統(tǒng)工程;2004年11期

4 都紅雯,楊威;我國對(duì)KMV模型實(shí)證研究中存在的若干問題及對(duì)策思考[J];國際金融研究;2004年11期

5 朱小宗;張宗益;耿華丹;吳俊;;現(xiàn)代信用風(fēng)險(xiǎn)度量模型的實(shí)證比較與適用性分析[J];管理工程學(xué)報(bào);2006年01期

6 謝赤;徐國嘏;;銀行信用風(fēng)險(xiǎn)度量Credit Metrics~(TM)模型與CPV模型比較研究[J];湖南大學(xué)學(xué)報(bào)(自然科學(xué)版);2006年02期

7 王春峰,萬海暉,張維;商業(yè)銀行信用風(fēng)險(xiǎn)評(píng)估及其實(shí)證研究[J];管理科學(xué)學(xué)報(bào);1998年01期

8 張紅舸;夏佳南;;KMV信用風(fēng)險(xiǎn)度量模型在我國的適應(yīng)性研究[J];經(jīng)濟(jì)論壇;2006年11期

9 曹道勝;何明升;;商業(yè)銀行信用風(fēng)險(xiǎn)模型的比較及其借鑒[J];金融研究;2006年10期

10 陳靜;上市公司財(cái)務(wù)惡化預(yù)測(cè)的實(shí)證分析[J];會(huì)計(jì)研究;1999年04期

相關(guān)碩士學(xué)位論文 前4條

1 張義強(qiáng);我國上市公司信用風(fēng)險(xiǎn)度量研究[D];暨南大學(xué);2003年

2 陳捷;商業(yè)銀行信用風(fēng)險(xiǎn)度量及管理[D];首都經(jīng)濟(jì)貿(mào)易大學(xué);2003年

3 周陽;我國中小企業(yè)信用風(fēng)險(xiǎn)度量研究[D];浙江大學(xué);2006年

4 彭莉;基于KMV模型的商業(yè)銀行信用風(fēng)險(xiǎn)度量研究[D];中南大學(xué);2007年

，

本文編號(hào)：1803607