本文選題：整合風(fēng)險(xiǎn)　切入點(diǎn)：商業(yè)銀行　出處：《河南師范大學(xué)》2014年碩士論文

【摘要】：20世紀(jì)80年代以前，商業(yè)銀行主要是以單一、局部的管理方式來(lái)管理其所面對(duì)的金融風(fēng)險(xiǎn)。90年代以來(lái)，隨著金融技術(shù)的進(jìn)步，經(jīng)濟(jì)全球化趨勢(shì)的迅速蔓延，兩個(gè)非常嚴(yán)峻的現(xiàn)實(shí)擺在了銀行風(fēng)險(xiǎn)管理的面前。它們分別是：一，，經(jīng)濟(jì)的全球化速度加快以及行業(yè)集團(tuán)化的現(xiàn)象更加明顯；二，信息計(jì)算科學(xué)的廣泛使用。這些現(xiàn)實(shí)都使得商業(yè)銀行所面對(duì)的風(fēng)險(xiǎn)由單一的信用風(fēng)險(xiǎn)走向多樣化、復(fù)雜化。隨著巴塞爾新資本協(xié)議的頒布，商業(yè)銀行的風(fēng)險(xiǎn)管理模式也隨之改變，即從以往的單一的信用風(fēng)險(xiǎn)管理模式轉(zhuǎn)變?yōu)樾庞蔑L(fēng)險(xiǎn)、市場(chǎng)風(fēng)險(xiǎn)、操作風(fēng)險(xiǎn)三種風(fēng)險(xiǎn)并舉的全面的風(fēng)險(xiǎn)管理模式，同時(shí)指出各風(fēng)險(xiǎn)之間存在著某些相關(guān)性[1]。既然多種風(fēng)險(xiǎn)之間存在著相關(guān)性，那么銀行在風(fēng)險(xiǎn)管理方面不得不制定出更完善的應(yīng)對(duì)策略�；鞓I(yè)化的經(jīng)營(yíng)是這世紀(jì)所有金融行業(yè)的發(fā)展趨勢(shì)。在這種大的經(jīng)濟(jì)背景下，銀行業(yè)更是在混業(yè)經(jīng)營(yíng)方面表現(xiàn)最為突出的一個(gè)行業(yè)。由于不同的業(yè)務(wù)資本的組合而產(chǎn)生的混合風(fēng)險(xiǎn)使得單一的風(fēng)險(xiǎn)管理模式無(wú)法應(yīng)對(duì)，因此將各類風(fēng)險(xiǎn)進(jìn)行整合起來(lái)管理越來(lái)越引起研究者和風(fēng)險(xiǎn)監(jiān)管者的重視。因此，整合風(fēng)險(xiǎn)管理也成為當(dāng)代商業(yè)銀行風(fēng)險(xiǎn)管理的最為突出的發(fā)展趨勢(shì)。 本文首先對(duì)商業(yè)銀行所面對(duì)的三種風(fēng)險(xiǎn)進(jìn)行了概括性的分析，分別介紹了它們各自的金融學(xué)含義、特征并且簡(jiǎn)明扼要的總結(jié)了實(shí)踐中常用的度量方法。然后簡(jiǎn)單介紹了VaR、CVaR，Copula函數(shù)的相關(guān)內(nèi)容以及Monte Carlo模擬的基本步驟。在過去的很長(zhǎng)一段時(shí)間里，大多數(shù)的研究者和風(fēng)險(xiǎn)監(jiān)管者使用的都是將不同風(fēng)險(xiǎn)值簡(jiǎn)單的相加，以用來(lái)估計(jì)總體風(fēng)險(xiǎn)，但是這種方法卻大大的高估了整合的風(fēng)險(xiǎn)值。基于Copula函數(shù)的整合風(fēng)險(xiǎn)的度量模型可以更加準(zhǔn)確的描述不同風(fēng)險(xiǎn)邊際分布間的相依結(jié)構(gòu)，那么對(duì)風(fēng)險(xiǎn)的度量模型不再是對(duì)單個(gè)的風(fēng)險(xiǎn)的度量，而是整體考慮了金融機(jī)構(gòu)所面對(duì)的三種主要風(fēng)險(xiǎn)。本文在整合度量商業(yè)銀行所面對(duì)的三種風(fēng)險(xiǎn)時(shí)采用了Copula函數(shù)和CVaR方法。同時(shí)分別用Normal copula和t-copula函數(shù)來(lái)描述三種風(fēng)險(xiǎn)之間的相依結(jié)構(gòu)，同時(shí)構(gòu)造了它們的聯(lián)合分布函數(shù)，最后通過Monte Carlo模擬來(lái)估計(jì)不同的相依結(jié)構(gòu)下整合風(fēng)險(xiǎn)的CVaR值。通過此模型可以計(jì)算出商業(yè)銀行所面對(duì)的這三種主要風(fēng)險(xiǎn)的的整合CVaR值和VaR值，從而可以比較出兩者的優(yōu)劣，同時(shí)本文也研究了不同的權(quán)重組合CVaR值的影響。
[Abstract]:Before the 1980s, commercial banks managed the financial risks they faced mainly in a single and partial way. Since the 1990s, with the development of financial technology, the trend of economic globalization has spread rapidly. Two very serious realities are facing the risk management of banks. They are: first, the rapid pace of economic globalization and the phenomenon of industry collectivization is more obvious; second, With the widespread use of information computing science, these realities have made the risks faced by commercial banks diversified and complicated from a single credit risk. With the promulgation of the New Basel Capital Accord, The risk management mode of commercial banks has also changed, that is, from the former single credit risk management mode to the comprehensive risk management mode of credit risk, market risk and operational risk. At the same time, it is pointed out that there is some correlation between various risks [1]. Well, banks have to work out a more complete coping strategy in risk management. Mixed operation is the development trend of all financial industries in this century. Under such a large economic background, The banking industry is the most outstanding industry in the aspect of mixed operation. Because of the mixed risk caused by different portfolio of business capital, a single risk management model can not cope with it. Therefore, the integration of various types of risk management has attracted more and more attention of researchers and risk regulators. Therefore, integrated risk management has become the most prominent development trend of risk management in contemporary commercial banks. In this paper, three kinds of risks faced by commercial banks are analyzed, and their respective financial meanings are introduced respectively. Features and a brief summary of the commonly used measurement methods in practice. Then, this paper briefly introduces the relevant contents of the Monte Carlo function and the basic steps of Monte Carlo simulation. In the past a long time, Most researchers and risk regulators use simple sums of different risk values to estimate the overall risk. However, this method greatly overestimates the risk value of integration. The model based on Copula function can more accurately describe the dependent structure of the marginal distribution of different risks. So the risk measurement model is no longer a measure of a single risk, In this paper, Copula function and CVaR method are used to measure the three kinds of risks faced by commercial banks. Normal copula and t-copula function are used to describe the three kinds of wind, respectively. The dependent structure between risks, At the same time, the joint distribution function is constructed, and the CVaR value of integration risk under different dependent structures is estimated by Monte Carlo simulation. By this model, the integrated CVaR value and VaR value of the three main risks faced by commercial banks can be calculated. So we can compare the advantages and disadvantages of the two. At the same time, this paper also studies the influence of different weight combination CVaR value.
【學(xué)位授予單位】：河南師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：F224;F830.3

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