本文選題：商業(yè)銀行 + 信用風(fēng)險(xiǎn)�。� 參考：《安徽大學(xué)》2014年碩士論文

【摘要】：隨著金融市場(chǎng)的迅速發(fā)展,各國(guó)的金融監(jiān)管機(jī)構(gòu)對(duì)風(fēng)險(xiǎn)的管理和監(jiān)控也逐步加強(qiáng),信用風(fēng)險(xiǎn)因其重要性及影響深遠(yuǎn),成為風(fēng)險(xiǎn)管理的重中之重。特別是對(duì)于商業(yè)銀行來(lái)說(shuō),能否有效管理和控制信用風(fēng)險(xiǎn)會(huì)對(duì)其盈利水平和穩(wěn)健能力產(chǎn)生決定性的影響。因此,從2004年新資本協(xié)議出臺(tái)到2010年巴塞爾協(xié)議III的問(wèn)世,負(fù)責(zé)監(jiān)控發(fā)達(dá)國(guó)家銀行業(yè)風(fēng)險(xiǎn)的巴塞爾委員會(huì)在不斷總結(jié)風(fēng)險(xiǎn)管理經(jīng)驗(yàn)的基礎(chǔ)上,為國(guó)際銀行的信用風(fēng)險(xiǎn)管理提出意見(jiàn)的同時(shí),也加強(qiáng)了對(duì)商業(yè)銀行的風(fēng)險(xiǎn)控制的要求,設(shè)定了更高的標(biāo)準(zhǔn)。在巴塞爾新資本協(xié)議中涉及了關(guān)于商業(yè)銀行信用風(fēng)險(xiǎn)管理的關(guān)鍵內(nèi)容即內(nèi)部評(píng)級(jí)法,其主要內(nèi)容就是測(cè)算借款人的違約概率。 近年來(lái),我國(guó)商業(yè)銀行也加強(qiáng)了對(duì)信用風(fēng)險(xiǎn)的管理和監(jiān)控,大力推進(jìn)內(nèi)部評(píng)級(jí)體系建設(shè),信用風(fēng)險(xiǎn)管理水平明顯提高,但國(guó)內(nèi)大部分商業(yè)銀行,特別是中小商業(yè)銀行距新資本協(xié)議以及巴塞爾協(xié)議III的要求和國(guó)際銀行業(yè)的先進(jìn)水平仍有一段距離。國(guó)外的先進(jìn)銀行對(duì)信用風(fēng)險(xiǎn)管理的研究已經(jīng)有很長(zhǎng)一段時(shí)間的歷史,并積累了研究數(shù)據(jù),因此信用風(fēng)險(xiǎn)管理的模型能夠不斷推陳出新并得以應(yīng)用。我國(guó)商業(yè)銀行應(yīng)當(dāng)結(jié)合我國(guó)的經(jīng)濟(jì)環(huán)境與金融發(fā)展程度,借鑒與吸收國(guó)外銀行先進(jìn)的信用風(fēng)險(xiǎn)管理經(jīng)驗(yàn)與思想,研究與開(kāi)發(fā)能夠適用于我國(guó)的優(yōu)秀的信用風(fēng)險(xiǎn)違約概率判別及預(yù)測(cè)模型。 本文從描述信用風(fēng)險(xiǎn)的特征和違約的概念入手,對(duì)信用風(fēng)險(xiǎn)度量方法進(jìn)行綜述。為了清晰地展現(xiàn)信用風(fēng)險(xiǎn)度量方法發(fā)展的歷程以及各種方法的特點(diǎn),將信用風(fēng)險(xiǎn)度量方法分為古典方法、傳統(tǒng)方法和現(xiàn)代方法。本文的實(shí)證分析,是以在我國(guó)證券市場(chǎng)2011年和2012年被披露發(fā)生ST的公司作為違約公司的樣本,以隨機(jī)抽取非ST的上市公司作為非違約公司的樣本,并將樣本分為訓(xùn)練集和測(cè)試集,并通過(guò)財(cái)務(wù)指標(biāo)組間的均值檢驗(yàn)和相關(guān)性檢驗(yàn)的篩選,選取8個(gè)財(cái)務(wù)指標(biāo)(資產(chǎn)負(fù)債率、資本積累率、總資產(chǎn)增長(zhǎng)率、財(cái)務(wù)杠桿系數(shù)、營(yíng)業(yè)收入現(xiàn)金比率、營(yíng)業(yè)利潤(rùn)率、應(yīng)收賬款周轉(zhuǎn)率、固定資產(chǎn)周轉(zhuǎn)率)作為建立信用風(fēng)險(xiǎn)度量模型的自變量,然后分別建立Bayes判別模型、Logisitic模型以及BP神經(jīng)網(wǎng)絡(luò)模型,對(duì)樣本公司的信用違約進(jìn)行判別和預(yù)測(cè)。從實(shí)證結(jié)果來(lái)看,Bayes判別模型在對(duì)上市公司信用違約的判別和預(yù)測(cè)時(shí)效果相對(duì)不太理想,Logisitic回歸模型和神經(jīng)網(wǎng)絡(luò)模型更勝一籌,其中,Logisitic回歸模型,對(duì)上市公司信用違約的預(yù)測(cè)性比神經(jīng)網(wǎng)絡(luò)模型更高,從而認(rèn)為L(zhǎng)ogisitic回歸模型是這三者之中最優(yōu)的信用風(fēng)險(xiǎn)違約概率計(jì)量模型。 最后,針對(duì)我國(guó)商業(yè)銀行信用風(fēng)險(xiǎn)管理存在的信用風(fēng)險(xiǎn)管理技術(shù)相對(duì)落后、不具備信用數(shù)據(jù)庫(kù)、缺乏成熟的信用評(píng)級(jí)機(jī)構(gòu)等問(wèn)題,提出建立先進(jìn)的適合我國(guó)商業(yè)銀行的信用風(fēng)險(xiǎn)模型、建立和完善商業(yè)銀行信用數(shù)據(jù)庫(kù)、以及建立具有專業(yè)素質(zhì)的第三方信用評(píng)級(jí)機(jī)構(gòu)等建議。
[Abstract]:With the rapid development of the financial market, the management and monitoring of risk is gradually strengthened by financial regulators in various countries. Credit risk has become the most important factor in risk management because of its importance and far-reaching impact. Especially for commercial banks, the effective management and control of the risk of credit will produce a decision on its profitability and stability. Therefore, from the introduction of the new capital agreement in 2004 to the advent of the Basel agreement III in 2010, the Basel Committee, which is responsible for monitoring the risk of the banking industry in the developed countries, on the basis of constantly summarizing the experience of risk management, puts forward some suggestions for the management of the credit risk of the international bank, and also strengthens the risk control of the commercial banks. In the new Basel capital agreement, the key content of the credit risk management of commercial banks is the internal rating method, which is the main content of calculating the default probability of the borrowers.
In recent years, China's commercial banks have also strengthened the management and monitoring of credit risk, vigorously promoted the construction of the internal rating system and improved the level of credit risk management, but most of the domestic commercial banks, especially the small and medium-sized commercial banks, still have a new capital agreement, the requirements of the Basel agreement III and the advanced level of the international banking industry. Foreign advanced banks have been studying credit risk management for a long time, and accumulated research data. Therefore, the model of credit risk management can be constantly updated and applied. China's commercial banks should combine with the economic environment and financial development of our country, draw lessons from and absorb foreign banks first. The credit risk management experience and thinking, research and development can be applied to China's excellent credit risk default probability discrimination and prediction model.
This paper, starting with the description of the characteristics of credit risk and the concept of default, summarizes the method of credit risk measurement. In order to clearly show the course of the development of the method of credit risk measurement and the characteristics of various methods, the methods of credit risk measurement are divided into classical methods, traditional methods and modern methods. The empirical analysis of this paper is based on me. In 2011 and 2012, the national securities market was disclosed as a sample of default companies, which randomly selected non ST listed companies as samples of non default companies, and divided the samples into training sets and test sets, and selected 8 financial indicators (assets and liabilities) by means of the mean test and the screening of correlation inspection among the financial indicators. Rate, capital accumulation rate, total asset growth rate, financial leverage coefficient, operating income cash ratio, operating profit rate, account receivable turnover rate, fixed assets turnover rate as independent variables for establishing credit risk measurement model, and then establish Bayes discriminant model, Logisitic model and BP neural network model respectively, to Sample Firms's credit violation. According to the empirical results, the Bayes discriminant model has a relatively poor effect on the discrimination and prediction of the listed companies' credit default, and the Logisitic regression model and the neural network model are better, and the Logisitic regression model has a higher predictability than the neural network model for the credit default of the listed companies. It is considered that the Logisitic regression model is the best credit risk default probability measurement model among the three.
Finally, in view of the relative backwardness of credit risk management technology in the credit risk management of China's commercial banks, the lack of credit database and the lack of mature credit rating agencies, it is proposed to establish an advanced credit risk model suitable for China's commercial banks, to establish and perfect the credit database of commercial banks, and to establish a professional professional bank. The quality of the third party credit rating agencies and other recommendations.
【學(xué)位授予單位】：安徽大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：F224;F832.33

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 石曉軍,陳殿左;債權(quán)結(jié)構(gòu)、波動(dòng)率與信用風(fēng)險(xiǎn)——對(duì)中國(guó)上市公司的實(shí)證研究[J];財(cái)經(jīng)研究;2004年09期

2 張鳴,程濤;上市公司財(cái)務(wù)預(yù)警實(shí)證研究的動(dòng)態(tài)視角[J];財(cái)經(jīng)研究;2005年01期

3 鄭茂;我國(guó)上市公司財(cái)務(wù)風(fēng)險(xiǎn)預(yù)警模型的構(gòu)建及實(shí)證分析[J];金融論壇;2003年10期

4 甄士龍;黎艷;;用Logistic模型估計(jì)企業(yè)的違約概率[J];廣西金融研究;2008年11期

5 劉迎春;;基于Logistic回歸的中國(guó)上市公司信用風(fēng)險(xiǎn)度量研究[J];黑龍江對(duì)外經(jīng)貿(mào);2010年11期

6 于立勇;商業(yè)銀行信用風(fēng)險(xiǎn)評(píng)估預(yù)測(cè)模型研究[J];管理科學(xué)學(xué)報(bào);2003年05期

7 王春峰,萬(wàn)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 章忠志,符林,唐煥文;基于人工神經(jīng)網(wǎng)絡(luò)的商業(yè)銀行信用風(fēng)險(xiǎn)模型[J];經(jīng)濟(jì)數(shù)學(xué);2003年03期

9 吳世農(nóng),盧賢義;我國(guó)上市公司財(cái)務(wù)困境的預(yù)測(cè)模型研究[J];經(jīng)濟(jì)研究;2001年06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

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