本文關(guān)鍵詞： 上市銀行 信用風(fēng)險(xiǎn) KMV模型　出處：《山東大學(xué)》2012年碩士論文　論文類型：學(xué)位論文

【摘要】：引發(fā)國際性信用危機(jī)的往往是少數(shù)幾家存在信用風(fēng)險(xiǎn)的銀行,從美國在20世紀(jì)初由長期資本管理公司引發(fā)的華爾街危機(jī),到雷曼兄弟引發(fā)的全球性金融危機(jī),都是由于少數(shù)存在信用風(fēng)險(xiǎn)的金融機(jī)構(gòu)導(dǎo)致的。信用違約風(fēng)險(xiǎn)的發(fā)生對(duì)整個(gè)金融系統(tǒng)產(chǎn)生巨大沖擊,從而導(dǎo)致了金融危機(jī)的發(fā)生,因此測(cè)算違約風(fēng)險(xiǎn)水平,防范信用違約風(fēng)險(xiǎn)對(duì)金融系統(tǒng)的穩(wěn)定至關(guān)重要。 在國外銀行業(yè)信用違約風(fēng)險(xiǎn)的測(cè)算方法已經(jīng)成熟,并建立了詳細(xì)和龐大的數(shù)據(jù)庫資料,對(duì)于從各個(gè)角度評(píng)價(jià)銀行信用風(fēng)險(xiǎn)提供了依據(jù)。而對(duì)我國的銀行業(yè)樣本的信用風(fēng)險(xiǎn)分析,不同學(xué)者采用了不同的方法嘗試對(duì)銀行違約風(fēng)險(xiǎn)的測(cè)算,主要集中于對(duì)幾大模型的有效性分析和適應(yīng)性修正的研究上。評(píng)價(jià)指標(biāo)主要是傳統(tǒng)的不良資產(chǎn)比率和資本充足率,對(duì)銀行業(yè)信用風(fēng)險(xiǎn)的評(píng)價(jià)主要采用五級(jí)分類法,相比新《巴塞爾協(xié)議》對(duì)全球銀行業(yè)信用評(píng)級(jí)體系的要求還有一定差距,如大多數(shù)發(fā)達(dá)國家所采用的KMV評(píng)級(jí)。 鑒于信用違約風(fēng)險(xiǎn)測(cè)算在金融系統(tǒng)的重要作用,本文運(yùn)用了最新的計(jì)量模型KMV模型測(cè)算了我國銀行的違約風(fēng)險(xiǎn),選擇信用違約距離、資產(chǎn)波動(dòng)率、股權(quán)波動(dòng)率等幾大指標(biāo)評(píng)價(jià)信用風(fēng)險(xiǎn),并對(duì)影響我國銀行違約風(fēng)險(xiǎn)的因素進(jìn)行了分析。本文首先選擇了已經(jīng)上市的16家銀行4年的數(shù)據(jù)作為研究對(duì)象,并按照因子分類法對(duì)其進(jìn)行分組,分為傳統(tǒng)國有商業(yè)銀行、股份制商業(yè)銀行及城市商業(yè)銀行,并在組內(nèi)根據(jù)打分結(jié)果做出對(duì)比,其次,本文對(duì)KMV模型做出幾點(diǎn)適應(yīng)性修正,并提取樣本的股權(quán)數(shù)據(jù)和資產(chǎn)數(shù)據(jù),分別計(jì)算樣本的信用違約距離。最后本文結(jié)合因子的分組,對(duì)三組銀行的信用違約距離的計(jì)算結(jié)果、對(duì)銀行業(yè)的整體走勢(shì)、對(duì)組內(nèi)各個(gè)樣本的變化分別進(jìn)行分析,并對(duì)其做出預(yù)測(cè)。 本文的研究結(jié)果表明,上市的16家銀行整體信用風(fēng)險(xiǎn)狀況良好,其中股份制商業(yè)銀行的信用風(fēng)險(xiǎn)最小,發(fā)展較穩(wěn)定,說明其信用風(fēng)險(xiǎn)機(jī)制已經(jīng)趨于成熟和完善,傳統(tǒng)國有銀行信用風(fēng)險(xiǎn)其次,但是各家銀行的信用風(fēng)險(xiǎn)變化較大,順周期波動(dòng)的特征明顯。同時(shí)暴露出地方性商業(yè)銀行的信用風(fēng)險(xiǎn)整體較大,說明其信用風(fēng)險(xiǎn)控制機(jī)制還很不完善,這也為各地不斷成立的地方性金融機(jī)構(gòu)敲響了警鐘。
[Abstract]:The international credit crisis is often triggered by a few banks with credit risks, from the Wall Street crisis triggered by long-term capital management companies in the United States in early 20th century to the global financial crisis triggered by Lehman Brothers. The occurrence of credit default risk has a huge impact on the entire financial system, resulting in the occurrence of financial crisis, so calculate the level of default risk. Preventing the risk of credit default is crucial to the stability of the financial system. In foreign banking credit default risk measurement method has been mature, and has established a detailed and huge database. For the evaluation of bank credit risk from various angles, the credit risk analysis of banking samples in China, different scholars have adopted different methods to try to measure the risk of bank default. It mainly focuses on the effectiveness analysis and adaptive modification of several models. The evaluation indicators are mainly the traditional non-performing assets ratio and capital adequacy ratio. The evaluation of banking credit risk mainly adopts the five-level classification method, compared with the requirements of the new Basel Accord on the global banking credit rating system, there is still a certain gap. Such as the KMV rating adopted by most developed countries. In view of the important role of credit default risk measurement in the financial system, this paper uses the latest measurement model KMV model to calculate the default risk of Chinese banks, choose the distance of credit default, asset volatility. Several indicators, such as equity volatility, evaluate credit risk, and analyze the factors that affect the default risk of Chinese banks. Firstly, this paper selects the data of 16 listed banks for 4 years as the research object. According to the classification of factors, it is divided into traditional state-owned commercial banks, joint-stock commercial banks and urban commercial banks. This paper makes several adaptive modifications to the KMV model and extracts the equity data and asset data of the sample to calculate the credit default distance of the sample. Finally this paper combines the grouping of factors. The calculation results of the credit default distance of the three groups of banks, the overall trend of the banking industry, the changes of each sample in the group are analyzed, and the prediction is made. The results of this paper show that the overall credit risk of the 16 listed banks is in good condition, in which the joint-stock commercial banks have the smallest credit risk and the development is relatively stable. It shows that its credit risk mechanism has become mature and perfect, the traditional state-owned bank credit risk is next, but the credit risk of each bank changes greatly. At the same time, the credit risk of the local commercial banks is relatively large, which indicates that the credit risk control mechanism is not perfect. This has also sounded the alarm bell for the local financial institutions that have been set up all over the world.
【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：F832.33;F224

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