本文選題：商業(yè)銀行 + 信用風(fēng)險(xiǎn)��； 參考：《西南財(cái)經(jīng)大學(xué)》2013年碩士論文

【摘要】：上世紀(jì)七十年代由于金融自由化發(fā)展和金融管制放松,使各金融機(jī)構(gòu)的業(yè)務(wù)迅速發(fā)展,趨利性的金融衍生產(chǎn)品迅速出現(xiàn)。金融業(yè)的相關(guān)風(fēng)險(xiǎn)不斷暴露,不斷引發(fā)金融危機(jī),如亞洲金融危機(jī)、歐洲貨幣危機(jī)、美國次貸危機(jī)。尤其是美國次貸危機(jī),被前美聯(lián)儲(chǔ)主席格林斯潘認(rèn)為是“百年一遇”的全球性金融風(fēng)暴。有多方面原因?qū)е略摻鹑谖C(jī)的出現(xiàn),但究其根源,主要是信用風(fēng)險(xiǎn)管理方面的失控。 這些由于金融創(chuàng)新而爆發(fā)的金融危機(jī)使世界經(jīng)濟(jì)不斷遭到巨大的影響。為此,國際金融界各監(jiān)管部門對(duì)風(fēng)險(xiǎn)監(jiān)管提高了重視。國際銀行監(jiān)管機(jī)構(gòu)于2010年9月出臺(tái)的《巴塞爾新資本協(xié)議》,對(duì)銀行風(fēng)險(xiǎn)的控制更加關(guān)注,提出的資本和流動(dòng)性監(jiān)管標(biāo)準(zhǔn)比以前更加嚴(yán)格。 從國內(nèi)目前的研究現(xiàn)狀來看,我國銀行業(yè)信用評(píng)級(jí)體系只可達(dá)到基本的內(nèi)部評(píng)級(jí)法的要求,但是與高級(jí)內(nèi)部評(píng)級(jí)法的規(guī)定還相差較遠(yuǎn),遠(yuǎn)達(dá)不到規(guī)定的要求。我國商業(yè)銀行成立較晚,銀行業(yè)監(jiān)管方面不完善,信用風(fēng)險(xiǎn)管理水平落后,技術(shù)不發(fā)達(dá),體系不健全,沒有更好的方法量化信用風(fēng)險(xiǎn)的大小。 因此,借鑒并研究學(xué)習(xí)國際銀行業(yè)信用風(fēng)險(xiǎn)管理的相關(guān)先進(jìn)技術(shù)對(duì)我國銀行業(yè)抗風(fēng)險(xiǎn)能力的提高和信用風(fēng)險(xiǎn)管理的加強(qiáng)具有重要意義,在將來的國際競(jìng)爭(zhēng)中處于有利地位。結(jié)合我國國有商業(yè)銀行的具體情況以及面對(duì)當(dāng)前上市公司非交易性金融信貸資產(chǎn),應(yīng)用CreditMetrics模型進(jìn)行研究和計(jì)算,對(duì)于我國銀行業(yè)的信用風(fēng)險(xiǎn)水平度量具有重大意義。 本文以CreditMetrics模型的實(shí)證分析與應(yīng)用為導(dǎo)向,先后分析我國商業(yè)銀行信用風(fēng)險(xiǎn)方面的現(xiàn)狀、存在的問題和對(duì)我國銀行業(yè)在信用風(fēng)險(xiǎn)管理方面實(shí)踐的簡(jiǎn)要回顧基礎(chǔ)上,圍繞商業(yè)銀行如何利用信用風(fēng)險(xiǎn)的模型方法去度量其面對(duì)的非交易性金融資產(chǎn)信貸組合,如針對(duì)銀行貸款和非公開性私募債券的價(jià)值和風(fēng)險(xiǎn)進(jìn)行度量和分析。最后提出在我國如何更好的加強(qiáng)社會(huì)各層面的信用風(fēng)險(xiǎn)管理,以至于減少商業(yè)銀行及其他金融結(jié)構(gòu)等信用風(fēng)險(xiǎn),并對(duì)此提出一些政策和建議。 文章主要采用實(shí)證分析和數(shù)理模型分析的研究方法。主要基于金融學(xué)、信用風(fēng)險(xiǎn)管理、數(shù)理金融學(xué)等方面的相關(guān)理論知識(shí)和模型工具。通過對(duì)我國商業(yè)銀行信用風(fēng)險(xiǎn)管理現(xiàn)狀的分析,利用CreditMetrics模型及其他相關(guān)數(shù)理模型輔助工具,如蒙特卡洛模擬方法、Cholesky分解方法、Nelson-Siegel方法以及VaR方法和ES方法。在假設(shè)的非交易性金融信貸資產(chǎn)組合頭寸的信息下,來實(shí)證分析如果信貸質(zhì)量發(fā)生變化而導(dǎo)致2013年信用評(píng)級(jí)變化的信用風(fēng)險(xiǎn)水平,并結(jié)合目前我國當(dāng)前各層面存在的信用風(fēng)險(xiǎn)管理問題提出相應(yīng)的建議。 在信用風(fēng)險(xiǎn)度量的實(shí)證部分,基于我國十二家上市公司相關(guān)數(shù)據(jù)組成的信貸資產(chǎn)組合,應(yīng)用CreditMetrics模型進(jìn)行該信貸資產(chǎn)組合的信用風(fēng)險(xiǎn)水平度量。但是,由于貸款不能公開交易信息,無法準(zhǔn)確獲得貸款市值及其一年內(nèi)貸款價(jià)值的波動(dòng)大小。對(duì)此,可以通過利用該債務(wù)公司的一些其他公開信息來估計(jì)其貸款市值。這些需要搜集的信息包括：選取的12家債務(wù)公司2012年的歷史股價(jià),債務(wù)公司主體長(zhǎng)期信用等級(jí)及其資產(chǎn)回收率,信用風(fēng)險(xiǎn)轉(zhuǎn)移矩陣和債券市場(chǎng)上的各信用評(píng)級(jí)的公司債券信息。 CreditMetrics模型最先由J.P.摩根同其他合伙人于1997年提出,用于計(jì)量貸款組合信用風(fēng)險(xiǎn)的新型內(nèi)控模型,考慮資產(chǎn)價(jià)值隨經(jīng)濟(jì)的時(shí)間變動(dòng)和信用等級(jí)的變化引起的資產(chǎn)總價(jià)值變動(dòng)。該模型基本思想是通過收集該債務(wù)公司的其他公開信息,并基于債務(wù)公司在一定期限內(nèi)(通常是1年)的某個(gè)市場(chǎng)風(fēng)險(xiǎn)因子的變動(dòng)情況,研究下一年因受違約事件或債務(wù)公司信用質(zhì)量導(dǎo)致的信用等級(jí)轉(zhuǎn)移、降級(jí)、升級(jí),從而影響資產(chǎn)組合的價(jià)值,以此計(jì)量該資產(chǎn)組合在第2年期末的市場(chǎng)價(jià)值。進(jìn)一步根據(jù)期末損失分布,求出一定置信水平下信貸資產(chǎn)組合可能發(fā)生的最大價(jià)值損失。 CreditMetrics模型對(duì)于信貸組合信用風(fēng)險(xiǎn)的度量應(yīng)用需要一些理論假設(shè)條件,這些假設(shè)包括：信貸資產(chǎn)的股票收益率呈標(biāo)準(zhǔn)正態(tài)分布,并服從標(biāo)準(zhǔn)的幾何布朗運(yùn)動(dòng)。不良貸款回收率與違約率不相關(guān)。非交易性金融資產(chǎn)組合中各筆貸款頭寸在研究期間保持不變。 論文具體研究了以下幾個(gè)問題： 第一,我國商業(yè)銀行信用風(fēng)險(xiǎn)的現(xiàn)狀和存在的問題,并簡(jiǎn)要回顧了我國已經(jīng)對(duì)此做出的管理實(shí)踐。 由于我國銀行業(yè)還沒有建立起先進(jìn)的信用風(fēng)險(xiǎn)資料的歷史數(shù)據(jù)庫,數(shù)據(jù)庫存在問題,導(dǎo)致信用風(fēng)險(xiǎn)的計(jì)量難度較大。商業(yè)銀行在長(zhǎng)期經(jīng)營中也暴露出大量信用風(fēng)險(xiǎn),僅從財(cái)務(wù)報(bào)表中就可以體現(xiàn)出商業(yè)銀行的不良貸款數(shù)額巨大而且在急劇上升,貸款呆帳準(zhǔn)備金比率較低,資產(chǎn)負(fù)債比例偏高,貸款量略大。在商業(yè)銀行的業(yè)務(wù)中,經(jīng)營監(jiān)管各環(huán)節(jié)仍能反應(yīng)出信用風(fēng)險(xiǎn)管理的缺失。 鑒于國際銀行業(yè)頻繁的發(fā)生動(dòng)蕩并出現(xiàn)危機(jī),國際監(jiān)管機(jī)構(gòu)不斷發(fā)布并出臺(tái)一些法律法規(guī),以此約束并規(guī)范銀行業(yè)的信用風(fēng)險(xiǎn)以及內(nèi)部控制。按照《巴塞爾新資本規(guī)定》的要求,我國一些商業(yè)銀行開始進(jìn)行內(nèi)部控制制度建設(shè)。在我國,中小企業(yè)作為特殊群體,近些年迅速發(fā)展。在促進(jìn)我國經(jīng)濟(jì)繁榮以及多元化發(fā)展的過程中,對(duì)中小企業(yè)發(fā)展的風(fēng)險(xiǎn)管理也采取了一系列的保護(hù)方法。 第二,基于CreditMetrics模型的信用風(fēng)險(xiǎn)度量實(shí)證分析與研究過程。 在信用風(fēng)險(xiǎn)度量的實(shí)證部分,在我國上市公司和資本市場(chǎng)中選取數(shù)據(jù),建立了信貸風(fēng)險(xiǎn)組合的度量框架。通過計(jì)算債務(wù)公司的歷史(2012年)股價(jià)收益率,利用蒙特卡洛方法模擬出與上年相關(guān)水平一致的收益率。利用穆迪公司發(fā)布的信用風(fēng)險(xiǎn)轉(zhuǎn)移矩陣,計(jì)算出由于信用評(píng)級(jí)變化對(duì)應(yīng)的不同收益率臨界值。通過Nelson-Siegel方法計(jì)算資本市場(chǎng)不同信用級(jí)別的債券收益率,從而得到2013年底信貸資產(chǎn)評(píng)級(jí)變化后的資產(chǎn)價(jià)值。再經(jīng)過搜集債務(wù)公司主體長(zhǎng)期信用等級(jí)和對(duì)應(yīng)的資產(chǎn)回收率,計(jì)算VaR值和ES值來度量信用風(fēng)險(xiǎn)水平。 實(shí)證結(jié)果認(rèn)為CreditMetrics模型對(duì)于研究我國信貸資產(chǎn)組合的信用風(fēng)險(xiǎn)水平的度量可行,但是仍然需要進(jìn)一步改進(jìn)。 第三,結(jié)合當(dāng)前我國各層面存在的信用風(fēng)險(xiǎn)管理問題提出相應(yīng)的建議。 評(píng)級(jí)機(jī)構(gòu)應(yīng)盡快建立信用風(fēng)險(xiǎn)數(shù)據(jù)庫,積極研究發(fā)明適合我國的內(nèi)部評(píng)級(jí)模型和體系,建立全面可靠的信用風(fēng)險(xiǎn)數(shù)據(jù)庫,并統(tǒng)計(jì)出適合國內(nèi)使用的風(fēng)險(xiǎn)轉(zhuǎn)移矩陣、資本回收率和債券遠(yuǎn)期貼現(xiàn)率等。 本文采用國際權(quán)威并流行的度量信用風(fēng)險(xiǎn)水平的CreditMetrics模型,由于歷史數(shù)據(jù)的搜集需要較強(qiáng)的數(shù)據(jù)庫支持,因此這種模型目前在我國只被少數(shù)大型銀行使用,還未在國內(nèi)廣泛推廣。筆者為此度量方法做嘗試,并驗(yàn)證易于計(jì)算并可行。本文可能存在的創(chuàng)新內(nèi)容有： 第一,文章對(duì)于信貸資產(chǎn)組合之間的相關(guān)系數(shù)用Cholesky方法分解,將每對(duì)債務(wù)公司之間的相關(guān)特征用Cholesky分解矩陣來表示。利用該分解矩陣將第一年債務(wù)公司之間的相關(guān)特征轉(zhuǎn)移到模擬的第二年(2013年)債務(wù)公司收益率的時(shí)間序列中,從而對(duì)模擬的2013年信貸資產(chǎn)的收益率進(jìn)行調(diào)整。相關(guān)性水平利用該上市公司的股價(jià)收益率相關(guān)性代替資產(chǎn)總價(jià)值的相關(guān)性,與經(jīng)濟(jì)變動(dòng)密切相聯(lián)系,時(shí)刻反應(yīng)經(jīng)濟(jì)的市場(chǎng)變動(dòng),具有較強(qiáng)的時(shí)效性。 第二,在計(jì)算不同信用評(píng)級(jí)的信貸資產(chǎn)遠(yuǎn)期利率水平時(shí),沒有采用傳統(tǒng)方法的零息票國庫券利率去貼現(xiàn)信貸資產(chǎn)的現(xiàn)金流,而是采用Nelson-Siegel模型,這樣可以針對(duì)不同信用評(píng)級(jí)的信貸資產(chǎn)分別計(jì)算其價(jià)值。為了使本文研究國內(nèi)信貸資產(chǎn)更有針對(duì)性,所以樣本數(shù)據(jù)選取的國內(nèi)各信用評(píng)級(jí)的公司債券,以此來計(jì)算國內(nèi)公司一年后變換到其他評(píng)級(jí)的價(jià)值。 第三,在計(jì)算信用風(fēng)險(xiǎn)水平時(shí),本文不僅采用了VaR值的計(jì)算,還計(jì)算了ES值�？紤]了所有信貸資產(chǎn)損失超過VaR值的小概率事件,對(duì)超過VaR值的所有信貸資產(chǎn)的損失值同樣重視。 在運(yùn)用CreditMetrics實(shí)證研究過程中發(fā)現(xiàn),該模型度量風(fēng)險(xiǎn)的精確性高度依賴于信用評(píng)級(jí)轉(zhuǎn)移矩陣和資本市場(chǎng)的債券遠(yuǎn)期利率的準(zhǔn)確性,所以評(píng)級(jí)機(jī)構(gòu)應(yīng)盡快建立相關(guān)數(shù)據(jù)庫并對(duì)此進(jìn)行統(tǒng)計(jì)計(jì)算。 計(jì)算結(jié)果有可能低估了《巴塞爾新資本協(xié)議》中經(jīng)濟(jì)資本的8%要求,因?yàn)槎攘窟^程中的置信水平選取較低,而且債券的到期日都在三年以上,而本文只研究了債券信貸資產(chǎn)發(fā)行一年后的信用風(fēng)險(xiǎn)水平。而且本文認(rèn)為CreditMetrics模型的損失分布函數(shù)有可能存在一個(gè)厚尾分布,今后可以基于此進(jìn)一步考慮CreditMetrics模型的實(shí)證計(jì)算。 本文的CreditMetrics模型使用股價(jià)市值,計(jì)算結(jié)果具有客觀性和前瞻預(yù)期性,貸款信息緊跟市場(chǎng)變動(dòng)而變動(dòng)。而且不僅考慮了貸款違約的風(fēng)險(xiǎn),也考慮了信貸資產(chǎn)質(zhì)量變化的風(fēng)險(xiǎn)。不僅可以用來度量信貸組合的信用風(fēng)險(xiǎn),也可度量單一貸款的信用風(fēng)險(xiǎn)。在度量信用風(fēng)險(xiǎn)時(shí),不僅利用VaR值來表示,而且還用ES值對(duì)VaR值進(jìn)行補(bǔ)充。指出銀行業(yè)的信用風(fēng)險(xiǎn)是我國商業(yè)銀行面臨的主要風(fēng)險(xiǎn)之一,當(dāng)前我國國內(nèi)銀行業(yè)要依據(jù)《巴塞爾新資本協(xié)議》的要求,完善我國銀行業(yè)的體制改革,制定前瞻性的銀行發(fā)展策略,從而引導(dǎo)銀行的改革與建設(shè)。
[Abstract]:Since the development of financial liberalization and the relaxation of financial regulation in the 1970s and the 1970s , the rapid development of the business of financial institutions and the rapid emergence of financial derivatives . The risks associated with the financial industry have been continuously exposed to the global financial crisis , such as the Asian financial crisis , the European monetary crisis and the US subprime crisis . Especially in the US subprime crisis , the former Federal Reserve Chairman , Greenspan , is regarded as a global financial storm of " one hundred years . " However , it is the root cause of the financial crisis , which is mainly the control of credit risk management .

The financial crisis triggered by financial innovation has caused the world ' s economy to continue to suffer . For this reason , regulators in the international financial sector have given greater attention to risk regulation . The Basel 2 Basel Capital Accord , issued in September 2010 , is more concerned about the control of banks ' risks , and the proposed standards of capital and liquidity supervision are more stringent than before .

In view of the present research situation in China , the credit rating system of China ' s banking industry can only meet the requirement of the basic internal rating method , but it is far from the requirement of the advanced internal rating method , but it can ' t meet the requirement . The bank ' s banking supervision is not perfect , the credit risk management level is backward , the technology is not developed , the system is not perfect , and the credit risk is not quantified .

Therefore , it is of great significance to learn from and study the relevant advanced technology of credit risk management in China ' s banking industry . It is of great significance to strengthen the anti - risk ability and credit risk management in China ' s banking industry in the future .

Based on the empirical analysis and application of the Credit Metrics model , this paper analyzes the current situation and existing problems of the credit risk of commercial banks in China , and measures and analyzes the value and risk of the bank loans and non - public private equity bonds . Finally , it puts forward some policies and suggestions on how to strengthen the credit risk management at all levels in our country so as to reduce the credit risk of commercial banks and other financial structures .

Based on the analysis of the current situation of credit risk management in China ' s commercial banks , this paper makes an empirical analysis on the credit risk level of credit rating change in 2013 based on the analysis of the current situation of credit risk management in China ' s commercial banks , such as Monte Carlo simulation method , cholesky decomposition method , Nelson - Siegel method , VaR method and ES method .

In the empirical part of the credit risk measure , based on the credit assets combination of twelve listed companies in our country , the credit risk level measure of the credit asset portfolio is made based on the Credit Metrics model . However , due to the fact that the loan cannot disclose the transaction information , it is impossible to accurately obtain the loan market value and the fluctuation size of the loan value within one year . The information that needs to be collected includes the historical stock price of the 12 debt companies selected in 2012 , the long - term credit rating of the debt company principal and its asset recovery rate , the credit risk transfer matrix and the corporate bond information of each credit rating on the bond market .

Credit Metrics Model is first proposed by J . P . Morgan and other partners in 1997 . It is used to measure the credit risk of loan portfolio . The basic idea of this model is to study the value of the asset portfolio at the end of the second year by collecting other public information of the debt company and based on the change of the credit rating caused by the credit quality of the debt company .

The Credit Metrics model requires some theoretical assumptions for the measurement of credit portfolio credit risk . These assumptions include : the stock yield of credit assets is standard normal distribution and follows the standard geometric Brownian motion . The non - performing loan recovery rate is not related to the default rate .

In this paper , the following problems are studied :

First , the present situation and existing problems of credit risk in commercial banks in China are briefly reviewed , and the management practice has been briefly reviewed .

Because China ' s banking has not established the advanced historical database of credit risk information , there is a problem in the database , which leads to the great difficulty of credit risk measurement . In the long run , commercial banks also exposed a large amount of credit risk . Only from the financial statements , the amount of non - performing loans of commercial banks is huge and the loan amount is slightly larger . In the business of commercial banks , the management and supervision links can still reflect the lack of credit risk management .

In view of the frequent turbulence and crisis in the international banking industry , the international regulatory agencies have issued and issued some laws and regulations to restrict and regulate the credit risk and internal control of the banking industry . In accordance with the requirements of the Basel 2 new capital requirement , some commercial banks in China have started the internal control system construction . In our country , small and medium - sized enterprises are developing rapidly in recent years . In the process of promoting the economic prosperity and diversification of our country , the risk management of the development of small and medium - sized enterprises has also taken a series of protection methods .

Secondly , based on the Credit Metrics model , the empirical analysis and research process of credit risk measurement .

In the empirical part of credit risk measurement , the data is selected in China ' s listed company and capital market , and the measure frame of credit risk combination is established . By calculating the yield of stock price in the history of the debt company ( 2012 ) , the yield of different yield corresponding to the previous year is simulated by Monte Carlo method . By means of the credit risk transfer matrix issued by Moody ' s Company , the asset value after the credit rating change has been calculated . Through the collection of the long - term credit rating and the corresponding asset recovery rate of the debt company , the VaR and ES value are calculated to measure the credit risk level .

The empirical results show that the Credit Metrics model is feasible to study the credit risk level of our country ' s credit portfolio , but we still need to improve further .

Thirdly , the paper puts forward some suggestions on the management of credit risk in all aspects of our country .

The rating agencies should establish the credit risk database as soon as possible , actively study the internal rating models and systems suitable for our country , establish a fully reliable credit risk database , and calculate the risk transfer matrix , capital recovery rate and long - term discount rate suitable for domestic use .

In this paper , based on the Credit Metrics model of the international authoritative and popular measure credit risk level , since the collection of historical data requires stronger database support , this model is only used by a small number of large banks in our country , and has not been widely promoted in China . The author attempts to do this and verifies that it is easy to calculate and feasible . The possible innovations in this paper are as follows :

First , the correlation coefficient between the credit asset portfolio is decomposed by the cholesky decomposition matrix , and the correlation between each pair of debt companies is represented by the cholesky decomposition matrix . By using the decomposition matrix , the correlation characteristics between the first year debt companies are transferred to the simulated second year ( 2013 ) debt company yield time series , so as to adjust the yield of the simulated 2013 credit assets .

Secondly , when calculating the forward interest rate level of credit assets with different credit rating , we do not adopt the traditional method ' s zero coupon treasury bond interest rate to discount the cash flow of the credit assets , but adopt the Nelson - Siegel model so that the value of the credit assets with different credit rating can be calculated respectively . In order to make the research study domestic credit assets more targeted , the domestic credit rating of the sample data is selected as the corporate bond of each credit rating , so as to calculate the value of the domestic company after one year after conversion to other ratings .

Thirdly , when calculating credit risk level , this paper not only adopts VaR calculation , but also calculates ES value . Considering the small probability event that all credit asset losses exceed VaR value , the loss value of all credit assets that exceed VaR value is also paid .

It is found that the accuracy of the model measure risk depends on the accuracy of the long - term interest rate of the credit rating transfer matrix and the capital market , so the rating agency should establish the relevant database as soon as possible and calculate the risk .

It is possible to underestimate the 8 % requirement of economic capital in Basel 2 Capital Accord , because the confidence level in the measurement process is lower , and the maturity of the bond is more than three years , and the credit risk level after one year of the issuance of the bond credit asset is studied .

The credit risk is one of the main risks faced by commercial banks in China . It is pointed out that the credit risk of banking is one of the main risks faced by commercial banks in China .

【學(xué)位授予單位】：西南財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：F832.4;F224

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