基于灰色关联分析商业银行信贷风险管理外文翻译.doc

1、毕业论文外文文献翻译毕业设计（论文）题目基于 Logistic模型商业银行信用风险评价的实证分析翻译题目基于灰色关联分析商业银行信贷风险管理学 院经济学院专 业金融专业姓 名 班 级 学 号 指导教师 基于灰色关联分析商业银行信贷风险管理1 介绍作为中国金融体系的重要组成部分,商业银行的操作的鲁棒性一直是金融监管的核心,这是关于整个金融系统的关键,国民经济的健康发展。目前,我国商业银行的大部分收入来自存贷款利率之间的区别。所以贷款的质量直接影响着银行的操作导致流行的信用风险的研究在理论和实践上。等问题在银行信贷资产质量低,大量的不良贷款逐渐挤压与金融市场环境的日益复杂和衍生金融工具的不断发展。

2、信用风险对商业银行吸引了越来越多的关注,必须正确理解。它不仅具有实际意义的加强国内商业银行的风险管理,但也有助于建设一个健全的和强大的金融监管体系,并最终导致日出发展道路。与此同时,信用风险的分析提供了一个参考实现不同的信贷和降低商业银行的信用风险,争取有利的商业银行的存款准备金率,以追求更好的发展条件下的中央银行实施不同的存款准备金率。对信用风险的研究,国内外学者主要集中在测量信贷与违约概率,违约风险的信用评级和专家判断。违约概率是债务人不能偿还债务的可能性预期(默认)。现有个人信贷资产违约概率模型主要包括默顿模型(默顿,1973);KMV模型的KMV公司(KMV,1993 - 1997);

3、物流模式;麦肯锡公司的信贷组合视图模型(威尔逊,1997)；瑞士银行模型,摩根大通和强度模型的信用度量模型(JP摩根,1997)。信用评级是由五个评价指标包括资本充足率、资产质量、管理、收益和流动性。骆驼评级体系是一套标准化和制度化的索引使用的综合评级系统目前全国性银行的货币监理署管理员直接在业务和商业银行和其他金融机构的信贷条件。专家判断方法的情况下使用上述模型需要的数据缺失的情况下,信用评级和违约等数据。这种方法主要是集中在信用风险评估从定性的角度。国内学者所做的风险评估研究。例如,总结了信用风险评估理论,提出了相关的理论基础和基本做法。这些模型和方法主要是用来衡量风险的大小,然后得出资产

4、组合的形式,以避免或减少信用风险的发生。在考虑具体的影响因素,研究低的数量。方舟子和曾庆红(2004)建立了信用风险评价函数使用多元统计方法对借款企业的财务数据,以提高企业的信用风险评估的科学性。白朱所做的研究信用风险影响因素,得出结论企业金融和宏观经济、地区和行业指数,对信贷风险在一定程度上产生影响。空置的基础上深入研究影响因素,本文研究了影响因素的减值贷款分析通过发生率不同行业使用灰色关联度分析方法识别方法。它增加了宏观经济因素,得出结论:信贷资产的比例所拥有的银行对信用风险的影响,并提供行业判断执行不同的信贷。根据实证分析结果,分析了银行信用风险的出现将从宏观的角度,提供了客观依据一些宏

5、观经济现象的存在。本文的结构如下:第二部分提出了发病率识别方法与时间序列相关银行信用风险根据行业和宏观经济因素。第三部分展示了实证研究和分析历史数据的交通银行理论模型,认为商业银行信用风险管理建议根据这项研究的结果;第四部分是调查的结论。2发生率识别方法信用风险是借款人或交易对手不能履行合同规定的义务(如维修)由于信贷质量变化,导致银行损失。本文选择减值贷款比率指的是信用风险大小的指标。减值贷款的银行包括现有的不良贷款和部分可能是未来的不良贷款。减值贷款的比例直接影响银行信贷的风险。如果减值贷款比率高,商业银行信贷风险相对大;如果减值贷款比率较低,商业银行信贷风险也相对较小。属性的银行贷款的行

6、业和宏观经济因素对银行信贷风险的影响,本文选择灰色系统理论的灰色关联分析方法来研究建立发生率识别方法。灰色关联度分析方法主要研究的发展趋势和发展因素的内部系统,广泛应用于社会系统、经济系统、农业系统、生态系统、教育系统。尽管总是使用回归分析方法来研究解释变量和变量之间的关系也广泛应用于复杂的客观经济变革现象,适用于灰色关联分析无论有多少数量的样品相比,回归分析和方差分析。计算也非常小,非常方便、定量和定性分析结果的偏差不会出现。根据现有数据灰度值的大型商业银行的发展和一些人为的因素,这种方法似乎尤其适用。减值贷款比率多年了的行为反应系统的数据序列和不同行业的银行贷款比例发生率相关因素序列建立模

7、型行业占比和银行不良贷款比率,然后影响因素上的探索性研究信用风险将会完成。与此同时,宏观经济因素将虚拟变量来测量相对贷款比率的变化通过使用绝对发生率识别方法对宏观经济和减值贷款。发病率识别模型的过程如下。定义7基于上面的发病率识别方法,我们可以计算灰色关联度对行业与减值贷款比率因素和宏观经济因素。根据绝对程度的灰色关联度m1,我们可以判断宏观经济因素会影响商业银行明显的减值贷款比率。因此,我们可以发现水平,宏观经济因素影响商业银行的信贷风险。根据结果的方法,不同的行业和宏观经济因素如何影响不良贷款比率可以分析。具体来说,影响商业银行信用风险的行业主要将通过灰色关联的发现不同行业之间的贷款规模和

8、减值贷款比率,这样我们可以提供定量参考商业银行信贷。宏观经济因素的影响,商业银行信贷风险将灰色系统理论的角度。风险控制系统,从行业的角度,行业风险因素可以根据灰色关联度确定不同行业因素和不良资产贷款所示:方程14根据不同行业的行业风险因素,实际的风险和预期的风险之间的偏差可以设置为：15个方程根据公式(10),行业关联度越大,风险越小偏差。在真正的贷款过程中,影响越大的行业信贷风险,管理部门应该更多的关注。偿还能力是需要特别指出。风险的控制下,该行业风险大相关性相对小范围也符合实际情况。因此,根据判断行业信贷风险的角度,行业因素的风险判断的结果可以扩展到风险控制。从行业因素、允许偏差的风险控制

9、可以定义,添加新的影响因素,使风险控制更加完美,更加合理。风险控制是风险管理的重要过程之一,所以我们可以控制的风险信用风险的行业因素根据灰色关联系数不同的行业因素造成的信用风险。以及常见的控制系统的对象,控制过程的控制系统结构如图1所示。风险控制制度的目的是确保在一定范围控制风险。对应于灰色控制理论,以确保风险控制的逐步稳定。2实证分析根据第二部分的结果,我们得出这样的结论:宏观经济影响的减值贷款与实际情况一致。在现实生活中,良好的宏观经济环境可以减少减值贷款。稳定的宏观经济环境,如宏观经济政策的扩张或整个经济社会的发展将促进企业经营,提高企业的管理。的条件下不存在债务危机和生存危机,企业将偿

10、还贷款和减值贷款比率最终将减少。关于上面的五个行业因素,制造业的贷款比例,权力、批发和零售和房地产业有各种对减值贷款比率的影响。电力行业最低限度影响商业银行的信贷风险。在田间小路的过程中,违约风险最小而银行为电力行业提供贷款。权力作为社会经济发展的一个基本能源是由国家控制,因此违约风险远低于其他行业。运输以及批发和零售贸易属于高风险信贷行业。这些行业的贷款必须严格对待,综合考虑不同的公司在这些行业的实际情况是不容忽视的,所以我们有理由确定贷款和贷款的数量。特别是房地产业的相关程度最大的五个行业和对不良资产的影响是最大的。近年来,房地产业发展在全国各地商业银行的主要力量。土地代理商寻求发展机遇无

11、处不在,到处都建了房子。房地产市场面临的高价格和高泡沫,更多的买家正在寻找更好的机会或者没有那么多钱买,房地产业的资金流动困难。银行将风险的承担者。房地产业是涉及广泛的连锁。资本流动的困难造成的体积小,房屋销售将导致整个行业链的中断,导致更多的高信用风险的银行。因此,银行应该适当减少信贷根据最新的房地产行业的宏观调控政策。银行应该确定为各行业的方向来分散风险。 灰色系统:理论与应用,2012,二(3),Jiajia Jin , Ziwen Yu , Chuanmin MiCommercial bank credit risk management based on grey incidence

12、 analysis1 IntroductionAs an important component of Chinas financial system, commercial banks operation robustness is always the core of financial supervision, which is the key concerning the whole financial system and the sound development of the national economy. At present, the majority of income

13、s of our country commercial banks come from the difference between deposit and loan rates. So the quality of loans has a direct impact on the operations of banks which result in popular research into the credit risk on theory and practice. The problems inside banks such as low quality of credit asse

14、t and huge number of bad loans is gradually extruding with the increasingly complexity of financial market environment and the continuous development of derivative financial instruments. Credit risk has attracted more and more attention of commercial banks which must be correctly understood. It not

15、only has the practical significance of strengthening the risk management of domestic commercial banks, but also helps the building of a sound and strong financial regulatory system and ultimately leads a sunrise developing path. Meanwhile, the analysis of credit risk provides a reference for the imp

16、lementation of different credit and reduces the commercial banks credit risk which will strive for favourable reserve ratio for the commercial bank so as to pursue a better development under the condition of different reserve requirements implemented by the Central Bank.On the study of credit risk,

17、domestic and foreign scholars mostly focused on the measurement of the credit default risk with default probability, the credit rating and expert judgment. The default probability is the possibility expected that the debtor cannot repay debts (default) on time. Existing individual credit asset defau

18、lt probability models mainly include Merton model (Merton, 1973); KMV model of KMV company (KMV, 1993-1997); Logistic model; Credit portfolio View model of McKinsey company (Wilson, 1997); CreditRisk + model of Swiss bank; Credit Metrics Model of JP Morgan and Intensity Model (J.P. Morgan, 1997). Cr

19、edit rating is made up of five assessment indicators including capital adequacy, asset quality, management, earnings and liquidity. CAMEL rating system is a set of standardized and institutionalized indexes of a comprehensive rating system currently used by the Comptroller of the Currency Administra

20、tor of National Banks directly at the business and credit condition of commercial banks and other financial institutions. Expert judging methods are used under the circumstances of the absence of the above model needing data, such as credit rating and defaulting data. This method is mostly focused o

21、n credit risk assessment from a qualitative angle.Domestic scholars have done research on risk assessment as well. For instance, Liu (2010) has summarized credit risk assessment theory and put forward relevant theoretical basis and basic practices. These models and methods are mostly used to measure

22、 the size of the risk and then conclude the form of the asset portfolio to avoid or reduce the occurrence of credit risk. On considering concrete influent factors, the amount of research done is low. Fang and Zeng (2004) have built the credit risk evaluation function using multivariate statistical m

23、ethods on the borrowing enterprises financial data so as to improve the scientificalness of enterprises credit risk assessment. Bai and Zhu (2008) have done research on the factors affecting credit risk and come to a conclusion that enterprise finance and macro-economy, region and industry index, ha

24、ve an influence on the credit risk to some extent. Based on the vacancy of in-depth research on the influencing factors, this article studies the influencing factors in impairment loans analyzed from different industries through the incidence identification method using the grey incidence analysis m

25、ethod. It adds the macroeconomic factor which concludes that the proportion of credit assets owned by the bank influences the credit risk and provides the industry judgment of executing difference credit. According to the empirical analysis result, how the bank credit risk emerges will be analyzed f

26、rom the macroscopic perspective and provides an objective basis for some existences of macroeconomic phenomenon.The structure of this paper is as follows: the second part puts forward the incidence identification method with time series related to the bank credit risk according to industry and macro

27、economic factors. The third part shows empirical research and analysis on the historical data with the Bank of Communications of the theoretical model and concludes that the commercial bank credit risk management advises according to the results of this study; the fourth part is the conclusion of in

28、vestigation.2 The incidence identification methodCredit risk is that the borrower or the counterparty is unable to perform a contract with the obligation prescribed (such as servicing) due to credit quality changes and causes a loss to the bank. This paper selects impairment loan ratio referring to

29、the index of credit risk size. The impairment loan of the bank includes the existing non-performing loans and the portion which is likely to be non-performing loans in the future. The proportion of the impairment loans influence the bank credit risk directly. If the impairment loan ratio is high, co

30、mmercial bank credit risk will be relatively big; if the impairment loan ratio is low, commercial bank credit risks will be relatively small.For the attribute of the industry and macroeconomic factor of bank loans towards the bank credit risk influence, this article chooses the grey incidence analys

31、is method of grey system theory to study establishing the incidence identification method. The grey incidence analysis method mainly researches the development trend and development factors of the internal system, which is widely used in the social system, economic system, agricultural system, ecosy

32、stem and education system. Although regression analysis method is always used to research the relationship between the explained variables and variables which is also widely used in complex objective economic change phenomenon, the grey incidence analysis is applicable no matter how many numbers of

33、the sample has compared to regression analysis and variance analysis. The computation is also very small, very convenient and the deviation of quantitative and qualitative analysis results would not appear. Based on the grey value of the existing data being large due to the development of commercial

34、 banks and some man-made factors, this method seems to be particularly applicable. The impairment loan ratio over years is took for the behavioural data sequence of reaction system and the bank loan proportion in different industries is took for the related factors sequence to build the incidence mo

35、del accounted for the industry ratio and impaired loan ratio in the bank, then the exploratory research on the factors influenced the credit risk will be done. At the same time, macroeconomic factor will be the virtual variable to measure relatively changes in the loan ratio by using an absolute inc

36、idence identification method with respect to the macroeconomic and impairment loan. The procedure of the incidence identification model is as follows.Definition 7Based on the incidence identification method above, we can compute the grey incidence degree with respect to the industry factor and macro

37、economic factor with the impairment loan ratio. According to the absolute degree of grey incidence m1, we can judge whether the macroeconomic factor can influence the impairment loan ratio of commercial bank obviously. Accordingly, we can find the level that macroeconomic factor influence the commer

38、cial banks credit risk.According to the results of the method, how the different industry and macroeconomic factors influence the impaired loan ratio can be analyzed.Specifically, the industry affecting commercial bank credit risk largely will be found out through the degree of grey incidence betwee

39、n the different industries loan scale and the impairment loan ratio, so that we can provide quantitative reference for commercial bank credit. Macroeconomic factors influence to the commercial bank credit risk will be demonstrated from the angle of the grey system theory.In the risk control system,

40、from the angle of industry, the industry risk factors can be determined according to grey correlation degree of different industries factors and bad assets loans which shown below: Equation 14 According to the industry risk factors of different industries, the deviation between actual risk and the e

41、xpected risk can be set as: Equation 15 According to formula (10), the greater the industry correlation is, the smaller the risk deviation is. In the real lending process, the greater the influence of the industry with the credit risk is, the greater attention of the management department should be.

42、 The repayment ability is needed to be noted specially. Under the control of risk, the industry with big correlation relatively has a small risk range which is also in accord with the actual situation.Therefore, according to the judgment of credit risk at the industry angle, the result of the risk j

43、udgment of industry factor can be extended to the risk control. From the industry factor, the allowed deviation of risk control can be defined so as to add new influent factors and make the risk control to be more perfect and more reasonable.The risk control is one of the important processes of risk

44、 management, so we can control the risk from the industry factor of credit risk according to grey incidence coefficient result of different industry factors with the credit risk. As well as the common control systems object, the control process has a control system structure as shown in Figure 1. Th

45、e purpose of risk control system is to ensure the risk is in certain limits under controlling. Corresponding to the grey control theory, that is to ensure the progressively stability of the risk control.2. Empirical analysisAccording to the result of the second part, we conclude that the macro econo

46、my affects the impairment loan which is consistent with the actual situation. In real life, a good macroeconomic circumstance can reduce the impairment loan. The steady macroeconomic circumstance, such as the expansion of macroeconomic policies or the whole development of economics in society will p

47、romote the enterprise operation and improve the management of the enterprise. Under the condition of the inexistence of debt crisis or existential crisis, the enterprise will repay the loan and the impairment loan ratio will reduce finally. With respect to the five industry factors as above, the loa

48、n proportion of manufacturing, power, wholesale and retail and realty business have various effects on the impairment loan ratio. The power industry minimally impacts the credit risk of the commercial bank. In the process of loaning, the default risk is smallest while the bank provides a loan to the

49、 electric power industry. Power as a fundamental energy of the social economic development is controlled by country, so the risk of default is much lower than any other industry. Transport as well as wholesale and retail trade belong to the high-risk credit industry. The loan to these industries must be rigorously treated and the comprehensive consideration of the actual situation of different companies within these industries cannot