本文選題：相依結(jié)構(gòu)Copula + 整合風(fēng)險(xiǎn)度量�。� 參考：《湖南師范大學(xué)》2014年碩士論文

【摘要】：隨著經(jīng)濟(jì)金融全球化的快速推進(jìn),金融市場(chǎng)的風(fēng)險(xiǎn)日趨復(fù)雜化,已從原來(lái)的僅僅考慮單一風(fēng)險(xiǎn)轉(zhuǎn)向綜合考慮多元化風(fēng)險(xiǎn),因此對(duì)風(fēng)險(xiǎn)進(jìn)行整合度量已成為必然�，F(xiàn)在,整合風(fēng)險(xiǎn)管理模式已引起了業(yè)界和學(xué)界的高度重視,整合風(fēng)險(xiǎn)管理模式的核心是對(duì)市場(chǎng)風(fēng)險(xiǎn)、操作風(fēng)險(xiǎn)和信用風(fēng)險(xiǎn)等不同類(lèi)別風(fēng)險(xiǎn)的整合管理。 目前金融機(jī)構(gòu)面臨的風(fēng)險(xiǎn)主要包括信用風(fēng)險(xiǎn)、市場(chǎng)風(fēng)險(xiǎn)和操作風(fēng)險(xiǎn),各風(fēng)險(xiǎn)之間存在相關(guān)性,并且具有非線性和尾部相關(guān)性等特點(diǎn)。傳統(tǒng)的在正態(tài)分布假設(shè)下的Pearson相關(guān)系數(shù)分析方法已不再適用,而Copula函數(shù)不受邊際分布選擇的限制,更好的描述風(fēng)險(xiǎn)間的相關(guān)結(jié)構(gòu)。所以,本文系統(tǒng)全面地研究了Copula在金融整合風(fēng)險(xiǎn)度量中的應(yīng)用。 理論部分,首先概括了Copula相關(guān)理論知識(shí),然后對(duì)常用Copula的相關(guān)性進(jìn)行深入的分析,并探討了如何對(duì)Copula模型進(jìn)行參數(shù)估計(jì)與模型選擇,最后詳細(xì)總結(jié)了風(fēng)險(xiǎn)度量指標(biāo)及其測(cè)量方法。 實(shí)證部分,鑒于度量整合風(fēng)險(xiǎn)必須在充分考慮各風(fēng)險(xiǎn)間相關(guān)性的基礎(chǔ)上進(jìn)行,本文分別從風(fēng)險(xiǎn)間的相關(guān)性和整合度量風(fēng)險(xiǎn)值這兩個(gè)方面對(duì)整合風(fēng)險(xiǎn)度量進(jìn)行實(shí)證研究。第一個(gè)實(shí)證,是分析信用利差與市場(chǎng)風(fēng)險(xiǎn)聯(lián)動(dòng)性,利用基于相關(guān)系數(shù)的Copula參數(shù)估計(jì)法研究相關(guān)結(jié)構(gòu),實(shí)證結(jié)果表明：兩者存在一定的正相關(guān),相關(guān)結(jié)構(gòu)可以較好地用Frank Copula函數(shù)來(lái)刻畫(huà)。在第二個(gè)實(shí)證中,我們以12家中國(guó)上市商業(yè)銀行為研究對(duì)象,首先確定各風(fēng)險(xiǎn)收益率的分布,然后利用Copula構(gòu)建相依結(jié)構(gòu),并選擇最優(yōu)模型,最后用蒙特卡羅模擬算法和重要性抽樣算法計(jì)算不同風(fēng)險(xiǎn)組合的VaR和CVaR,并用返回測(cè)試比較兩種算法的優(yōu)劣,研究結(jié)果表明當(dāng)損失較小時(shí),Monte Carlo計(jì)算的風(fēng)險(xiǎn)值更有效。以上工作為我國(guó)研究整合風(fēng)險(xiǎn)管理模型提供了較好地理論和技術(shù)支持。
[Abstract]:With the rapid development of economic and financial globalization, the risk of financial market is becoming more and more complicated. At present, the integrated risk management model has attracted great attention from the industry and academic circles. The core of the integrated risk management model is the integration management of different types of risks such as market risk, operational risk and credit risk. At present, the risks faced by financial institutions mainly include credit risk, market risk and operational risk, each risk has a correlation, and has the characteristics of nonlinear and tail correlation. The traditional Pearson correlation coefficient analysis method under the normal distribution hypothesis is no longer applicable, while the Copula function is not restricted by the marginal distribution, and it is better to describe the correlation structure between risks. Therefore, this paper systematically studies the application of Copula in financial integration risk measurement. In the theoretical part, we summarize the theory of Copula, then analyze the correlation of copula, and discuss how to estimate and choose the parameters of Copula model. Finally, the risk measurement index and its measurement method are summarized in detail. In the empirical part, in view of the fact that the integration risk must be measured on the basis of fully considering the correlation among the risks, this paper makes an empirical study on the integration risk measurement from the two aspects of the risk correlation and the integrated risk measurement. The first is to analyze the linkage between credit spread and market risk, and to study the correlation structure by Copula parameter estimation method based on correlation coefficient. The empirical results show that there is a certain positive correlation between them. The related structure can be well characterized by Frank Copula function. In the second empirical study, we take 12 Chinese listed commercial banks as the research object, first determine the distribution of the risk return rate, then use Copula to construct the dependent structure, and select the optimal model. Finally, the Monte Carlo simulation algorithm and importance sampling algorithm are used to calculate the VaR and Cvar of different risk combinations, and the advantages and disadvantages of the two algorithms are compared with the return test. The results show that the Monte Carlo method is more effective when the loss is small. The above work provides better theoretical and technical support for the study of integrated risk management model in China.
【學(xué)位授予單位】：湖南師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：F224;F830.9

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