【摘要】：為度量和計(jì)算信用組合風(fēng)險(xiǎn),美國J.P.MOrgan集團(tuán)推出了信用風(fēng)險(xiǎn)的量化度量模型CreditMetrics。CreditMetrics是基于VaR方法的信用風(fēng)險(xiǎn)度量模型,對(duì)組合風(fēng)險(xiǎn)中各資產(chǎn)收益的假設(shè)基于Gauss_Copula模型。本文介紹了針對(duì)CreditMetrics模型的普通蒙特卡洛方法、兩步重要抽樣方法對(duì)其尾部概率進(jìn)行估計(jì)。鑒于VaR與CVaR在風(fēng)險(xiǎn)管理中的重要作用,本文在指定概率水平下,給出了一種快速準(zhǔn)確計(jì)算VaR與CVaR的方法,并結(jié)合兩步重要抽樣方法做了具體說明。數(shù)值模擬方面,本文分別使用普通蒙特卡洛方法及文章提出的方法對(duì)信用風(fēng)險(xiǎn)組合VaR與CVaR值進(jìn)行模擬計(jì)算,并對(duì)標(biāo)準(zhǔn)差進(jìn)行比較。結(jié)果表明,本文提出的方法可更好地減小方差,提高計(jì)算準(zhǔn)確度。
[Abstract]:In order to measure and calculate the credit portfolio risk, J.P.MOrgan Group of the United States developed a quantitative credit risk measurement model CreditMetrics.CreditMetrics is a credit risk measurement model based on the VaR method, and the assumption of each asset return in the portfolio risk is based on the Gauss_Copula model. In this paper, the general Monte Carlo method for CreditMetrics model and the two-step important sampling method are introduced to estimate the tail probability. In view of the important role of VaR and CVaR in risk management, this paper presents a fast and accurate method for calculating VaR and CVaR at the specified probability level, and gives a detailed explanation with the two-step important sampling method. In the aspect of numerical simulation, the common Monte Carlo method and the method proposed in this paper are used to simulate and calculate the credit risk combination VaR and CVaR, and the standard deviation is compared. The results show that the proposed method can better reduce the variance and improve the accuracy of calculation.
【學(xué)位授予單位】：南京大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F830.91;O211.67
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本文編號(hào)：2297653