金融學(xué)外文翻譯--西方商業(yè)銀行信用風(fēng)險的度量

1、<p><b>　　中文2450字</b></p><p>　　出處：Santomero A M. Tolerance of the Credit Risks of Commercial Bank [J]. Journal of Financial Services Research, 1997, 12(2): 83-115.</p><p>　　外文原文

2、 </p><p>　　Tolerance of the Credit Risks of</p><p>　　Commercial Bank</p><p>　　AM Santomero</p><p>　　Commercial bank main model and method including tradition

3、 credit risks, credit of management measure method and based on VAR modern credit risks measure model. Among them, traditional credit risks measure method include expert system camphor tree law , credit point system and

4、neural network model, and based on VAR modern credit risks measure model include KMV model , surtax model , model of Credit-Metrics and credit risks of CSFP.</p><p>　　Expert system model law before one year,

5、 financial institution is it analyze or determine the nature analytical method not to weigh credit risks, enterprise of loan subjectively to rely on mainly, this kind of method is called the expert system models. Such as

6、 classical " 6C law " - By the morality about expert's foundation debtor (character), ability (capacity), capital (capital), pledge (collateral ) , management environment (condition ) and continuity (Contin

7、uity ) of undertaking six factor e</p><p>　　Credit point system to reflect debtor economic situation or influence debtor several indexes (such as the financial rate of the borrowing enterprise), credit of st

8、ate entrust to certain weight, receive and reflect credit the dividing value or the value of probability in breach synthetically of the credit state through some specific methods, and is it is it pay loan grant and loan

9、fixed price to determine to come compared with basic value it. Whether Z value model propose by Altman, adopt five fi</p><p>　　The neural network model roughly imitates the thought process of human brain and

10、 artificial intelligence system of the learning method. Neural algorithm of network whether one group input, carry on mathematics is it produce through transfer function one export to change and then. Foreign Altman and

11、Vrettos, Coats and Fans, etc all try to use this law, receive certain result. Someone uses such methods as the neural network, etc. to carry on the appraisal of risk of credit to the commercial bank t</p><p>

12、;　　The nerve network model imitates the person's artificial intelligence system of the brain thinking process and the study method mostly. The calculate way of the nerve network is an importation, and then pass to co

13、nvert the function to carry on mathematics conversion to produce an exportation of. Altman and Vrettos, Coates and Fans etc.excesses of the abroad all tried to make use of this method, being subjected to the certain resu

14、lt. Someone applies also the nerve network etc. the method carries </p><p>　　The Model of the KMV is the estimate to the borrow funds the business enterprise default all rate of method. First, it makes use o

15、f the Black- Stoles three option list price formula, the according to motion of the market value, the property value of the business enterprise property, expire time, the calm insurance to borrow interest rate and owe de

16、bt faces to be worth to estimate a market value of business enterprise ownership of a share and it undulates sex, compute a default implement( the Def</p><p>　　The model of Credit-Metdcs was credit risk that

17、 J · P root develops in 1997 to calculate model. It is an establishment on the usefulness foundation of reputation rating system of. The usefulness of reputation rating system means that investment in enterprise fai

18、lure, profits descend, margin outlet dried up etc. Reputation affairs appear towards fulfill contract influence of ability can in time and fittingly through variety body of its reputation grade. Because of the occurrence

19、 of reputation aff</p><p>　　The CSFP credit risk affixture calculates the Credit-Metrics dissimilarity that the model and conduct and actions stare at the city model( MTM), it is a break contract model( DM),

20、 it is not the rise and fall of the reputation rating and change with this related reputation excess fare to see as a part of VAR( credit risk) for lend money, but see do only is the market risk, it at any period conside

21、r to break contract and don't break contract these two kinds of affairs appearances only, loss that c</p><p>　　Above-mentioned three differentiations of the models can induce for six aspects of the follo

22、wing: First, definition aspect in the risk, the model of Credit-Metrics belongs to the model of MTM; The CSFP credit risk affixture calculates the model to belong to the model of DM; And model of KMV since can be conside

23、r as the model of MTM, also can be consider as the model of DM.Second, drive the factor aspect in the risk, in model of KMV and Credit Metrics, the risk drives the factor is business enter</p><p>　　Ever sinc

24、e that time in 1996, the Basel agreement ruled to use for assurance that an internal model of capital of the risk misted be to take Vary as the basal model, the Vary became most popular risk to manage the model currently

25、. Not only can carry on quantity's turn to the credit risk of debtor of the one, more important can carry on measure to concentrated risk of debtor of whole line and reputation of its related communities, but also ca

26、n press profession, term, category...etc. to carry on q</p><p><b>　　中文譯文</b></p><p>　　西方商業(yè)銀行信用風(fēng)險的度量</p><p>　　商業(yè)銀行信用管理的主要模型和方法包括傳統(tǒng)的信用風(fēng)險度量方法和基于VAR的現(xiàn)代信用風(fēng)險度量模型。其中，傳統(tǒng)的信用風(fēng)險度量方

27、法又包括專家系統(tǒng)模型法、信用評分法和神經(jīng)網(wǎng)絡(luò)模型，而基于VAR的現(xiàn)代信用風(fēng)險度量模型又包括KMV模型、Credit Metrics模型和CSFP信用風(fēng)險附加模型。</p><p>　　專家系統(tǒng)模型法是在20年前，金融機(jī)構(gòu)主要依賴主觀分析或定性分析方法衡量企業(yè)貸款的信用風(fēng)險，這種方法稱為專家系統(tǒng)模型。如經(jīng)典的“6C法”—由有關(guān)專家根據(jù)借款人的品德(character)、能力(capacity)、資本(capital

28、)、抵押品(collateral)、經(jīng)營環(huán)境(condition)和事業(yè)的連續(xù)性(Continuity)等六個因素評定其信用程度和綜合還款能力，決定是否最終發(fā)放貸款。經(jīng)典的信用分析存在著一些缺陷，主要表現(xiàn)在:①人的因素所帶來的風(fēng)險。②伴隨著嚴(yán)重的官僚主義作風(fēng)。</p><p>　　信用評分法是將反映借款人經(jīng)濟(jì)狀況或影響借款人信用狀況的若干指標(biāo)(如借款企業(yè)的財務(wù)比率)賦予一定權(quán)重，通過某些特定方法得到反映信用狀況的

29、信用綜合分值或違約概率值，并將其與基準(zhǔn)值相比來決定是否給予貸款發(fā)放以及貸款定價。Z值模型由Altman于1968年提出，采用五個財務(wù)指標(biāo)(這5個財務(wù)指標(biāo)是:Xl=營運(yùn)資本/總資產(chǎn)，X2=留存收益/總資產(chǎn)，X3=EBIT/總資產(chǎn)，X4=權(quán)益市場值/債務(wù)的賬面值，X5=銷售收入/總資產(chǎn))進(jìn)行加權(quán)計算，對借款企業(yè)實(shí)施信用評分，并將總分與臨界值比較，區(qū)分破產(chǎn)公司和非破產(chǎn)公司，對于破產(chǎn)企業(yè)將不發(fā)放貸款。1977年，Altman、Hardeman和

30、Nahayana對原始的Z值模型進(jìn)行了擴(kuò)展建立了分類準(zhǔn)確度較高的ZETA模型。1995年，對于非上市公司，Altman對Z值模型進(jìn)行了修改，得到了Z'計值模型。目前，這類模型的主要缺陷是缺乏必要的歷史紀(jì)錄材料。</p><p>　　神經(jīng)網(wǎng)絡(luò)模型是大致模擬人腦思維過程及學(xué)習(xí)方法的人工智能系統(tǒng)。神經(jīng)網(wǎng)絡(luò)的算法是一組輸入，再通過轉(zhuǎn)換函數(shù)進(jìn)行數(shù)學(xué)轉(zhuǎn)換產(chǎn)生出一個輸出。Altman和Vrettos、Coats和Fan

31、s等都嘗試過運(yùn)用此法，受到一定效果。有人也應(yīng)用神經(jīng)網(wǎng)絡(luò)等方法對商業(yè)銀行進(jìn)行信用風(fēng)險評價。然而，神經(jīng)模型的最大缺點(diǎn)就是其工作的隨機(jī)性較強(qiáng)，而且需要人工調(diào)試.耗費(fèi)大量人力與時間。</p><p>　　KMV模型是估計借款企業(yè)違約概率的方法。首先，它利用Black--Stoles三期權(quán)定價公式，根據(jù)企業(yè)資產(chǎn)的市場價值、資產(chǎn)價值的波動性、到期時間、無風(fēng)險借貸利率及負(fù)債的賬面價值估計出企業(yè)股權(quán)的市場價值及其波動性，再根據(jù)公

32、司的負(fù)債計算出公司的違約實(shí)施(Default Exercise Point，為企業(yè)一年以下短期債務(wù)的價值加上未清償長期債務(wù)賬面價值的一半)，然后計算借款人的違約距離，最后根據(jù)企業(yè)的違約距離與預(yù)期違約率(EDF)之間的對應(yīng)關(guān)系，求出企業(yè)的預(yù)期違約率。</p><p>　　Credit-Metrics模型是J·P·摩根1997年開發(fā)的信用風(fēng)險計量模型。其是建立在信用評級體系的有效性基礎(chǔ)上的。信用評

33、級體系的有效性是指企業(yè)投資失敗、利潤下降、融資渠道枯竭等信用事件對履約能力的影響都能及時恰當(dāng)?shù)赝ㄟ^其信用等級的變化體現(xiàn)出來。由于信用事件的發(fā)生，對企業(yè)的信用等級會產(chǎn)生影響，其信用工具的市場價值也必然發(fā)生相應(yīng)的變化。該模型是從資產(chǎn)組合的角度，而不是單一資產(chǎn)的角度來看待信用風(fēng)險的。它通過對比組合中各信用工具的邊際風(fēng)險貢獻(xiàn)(平均的邊際風(fēng)險貢獻(xiàn)=組合因增加某一信用工具而增加的風(fēng)險，該信用工具的市場價值)，進(jìn)而分析每種信用工具的信用等級、與其他資

34、產(chǎn)的相關(guān)系數(shù)以及其風(fēng)險暴露程度等各方面因素，可以看出各信用工具在整個組合的信用風(fēng)險中的作用，最終為投資者的信貸決策提供科學(xué)的量化依據(jù)。</p><p>　　CSFP信用風(fēng)險附加計量模型與作為盯市模型(MTM)的Credit-Metrics不同，它是一個違約模型(DM)，它不把信用評級的升降和與此相關(guān)的信用價差變化視為一筆貸款的VAR(信用風(fēng)險)的一部分，而只看作是市場風(fēng)險，它在任何時期只考慮違約和不違約這兩種事件

35、狀態(tài)，計量預(yù)期到和未預(yù)期到的損失，而不像在Credit--Metrics中度量預(yù)期到的價值和未預(yù)期到的價值變化。在CSFP信用風(fēng)險附加計量模型中，違約概率不再是離散的，而被模型化為具有一定概率分布的連續(xù)變量。每一筆貸款被視做小概率違約事件，并且每筆貸款的違約概率都獨(dú)立于其他貸款，這樣，貸款組合違約概率的分布接近泊松分布。CSFP信用風(fēng)險附加計量模型考慮違約概率的不確定性和損失大小的不確定性，并將損失的嚴(yán)重性和貸款的風(fēng)險暴露數(shù)量劃分頻段，

36、計量違約概率和損失大小可以得出不同頻段損失的分布，對所有頻段的損失加總即為貸款組合的損失分布。</p><p>　　上述三模型的區(qū)別可歸納為以下六個方面:第一，在風(fēng)險的界定方面，Credit-Metrics模型屬于MTM模型；CSFP信用風(fēng)險附加計量模型屬于DM模型；而KMV模型既可被當(dāng)作MTM模型，也可被當(dāng)作DM模型。第二，在風(fēng)險驅(qū)動因素方面，在KMV模型和Credit-Metrics中，風(fēng)險驅(qū)動因素是企業(yè)資產(chǎn)

37、價值及其波動性；而在CSFP信用風(fēng)險附加計量模型中，關(guān)鍵的風(fēng)險驅(qū)動因素是經(jīng)濟(jì)中可變的違約率均值。第三，在信用事件的波動性方面，在Credit-Metrics中，違約概率被模型化為基于歷史數(shù)據(jù)的固定的、或離散的值；而在KMV模型和CSFP信用風(fēng)險附加計量模型中，違約概率是可變的，但服從于不同的概率分布。第四，在信用事件的相關(guān)性方面，各模型具有不同的相關(guān)性結(jié)構(gòu)，KMV模型和Credit-Metrics是多變量正態(tài)；而CSFP信用風(fēng)險附加計量

38、模型是獨(dú)立假定或與預(yù)期違約率的相關(guān)性。第五，在回收率方面，在KMV模型的簡單形式中，回收率是不變的常數(shù)；在CSFP信用風(fēng)險附加計量模型中，損失的嚴(yán)重程度被湊成整數(shù)并劃分為不同的頻段，在頻段內(nèi)回收率是不變的；在KMV模型的最新版中，回收率是隨機(jī)的；在Credit</p><p>　　自從1996年，巴塞爾協(xié)議規(guī)定用于確定風(fēng)險的資本充足率內(nèi)部模型必須是以VaR為基礎(chǔ)的模型，VaR成為目前最為流行的風(fēng)險管理模型?；赩