本文選題：分位數(shù)回歸　切入點：條件VaR　出處：《南昌大學》2012年碩士論文　論文類型：學位論文

【摘要】：本文主要是應(yīng)用分位數(shù)回歸方法對條件VaR估計展開實證研究；并對Copula分位數(shù)回歸以及Copula函數(shù)在金融風險的尾部相關(guān)性分析中的應(yīng)用進行研究。 本文應(yīng)用分位數(shù)回歸方法給出條件VaR的估計方法,直接得到收益率在某置信水平下分位數(shù)的值,即在一定的條件下的VaR值。這樣就避免了分布是正態(tài)等假設(shè),而且計算相對容易。在含虛擬變量分位數(shù)回歸模型之下,實證分析上證指數(shù)和滬深指數(shù)在日內(nèi)波幅條件下的VaR,并與無條件模型及線性分位數(shù)回歸模型進行了比較。結(jié)果表明：含虛擬變量分位數(shù)回歸模型比無條件模型及線性分位數(shù)回歸模型能更好的度量風險。 本文分析研究Copula模型及Copula分位數(shù)回歸,推導了幾種常見的阿基米德Copula的分位數(shù)曲線。Copula分位數(shù)回歸把Copula理論和分位數(shù)回歸理論結(jié)合起來,能更好的度量變量之間的關(guān)系,特別是尾部的相關(guān)關(guān)系。因此本文利用Copula模型實證分析了滬深300和深證成指的尾部相關(guān)性,用尾部相關(guān)系數(shù)將上尾相關(guān)性量化,發(fā)現(xiàn)滬深300和深證成指有明顯的尾部相關(guān)性。并且用同種方法對創(chuàng)業(yè)板指數(shù)和中小板指數(shù)的相關(guān)性進行了分析,發(fā)現(xiàn)創(chuàng)業(yè)板指數(shù)和中小板指數(shù)也有明顯的尾部相關(guān)性。
[Abstract]:In this paper, the quantile regression method is applied to the empirical study of conditional VaR estimation, and the application of Copula quantile regression and Copula function in the tail correlation analysis of financial risk is studied. In this paper, the quantile regression method is used to estimate the conditional VaR, and the quantile value of the return rate at a certain confidence level is obtained directly, that is, the VaR value under certain conditions, thus avoiding the assumption that the distribution is normal. And it's relatively easy to calculate. In a regression model with virtual variables, This paper analyzes the VaR of Shanghai Stock Exchange Index and Shanghai and Shenzhen Index under the condition of intraday fluctuation, and compares them with unconditional model and linear quantile regression model. The results show that the quantile regression model with virtual variables is better than the unconditional model. Linear quantile regression model can better measure risk. In this paper, Copula model and Copula quartile regression are analyzed and studied. Several common Archimedes Copula quartile curve. Copula quartile regression combines Copula theory with quantile regression theory, which can better measure the relationship between variables. Especially the tail correlation. So we use Copula model to analyze the tail correlation between CSI 300 and Shenzhen Stock Exchange, and use the tail correlation coefficient to quantify the tail correlation. It is found that there is an obvious tail correlation between Shanghai and Shenzhen 300 and Shenzhen Stock Exchange, and the same method is used to analyze the correlation between the gem index and the small and medium-sized board index, and it is found that there is a significant tail correlation between the gem index and the small and medium-sized board index.
【學位授予單位】：南昌大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：F224;F832.51

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