本文選題：Kendall　切入點：tau　出處：《西南交通大學》2013年碩士論文　論文類型：學位論文

【摘要】：金融產(chǎn)品的多樣化,金融市場的不穩(wěn)定性等原因導致金融資產(chǎn)風險的不可控,因此研究風險價值具有很廣很深的實際意義。多資產(chǎn)組合風險因金融市場的靈活多樣,故其關鍵問題是動態(tài)相依機制的刻畫,而時變Copula函數(shù)能夠很好的刻畫變量間的相依機制。本文根據(jù)金融市場特征以及Copula建模理論,從Copula所導致的相依指標Kendall tau尾相關系數(shù)出發(fā),將構造方法和建模技術結合在一起,建立了時變Copula模型,成功描述了非線性的、非正態(tài)的時變相依機制,成功捕捉了下尾相依機制的時變性。主要工作包括： (1)從模式的識別、模型的檢驗等方面分析了阿基米德Copula模型的優(yōu)良性質,并結合金融數(shù)據(jù)的特性,說明了利用時變阿基米德Copula模型分析風險的可行性、有效性、優(yōu)越性。 (2)本文從阿基米德Copula函數(shù)與Kendall tau、尾相關系數(shù)之間的一一對應關系出發(fā),建立基于時變Kendall tau、時變尾相關系數(shù)的阿基米德Copula模型。 (3)假定Clayton Copula的時變參數(shù)為非線性的AR(1)模型,從Kendall tau和尾相關系數(shù)兩個角度出發(fā),分別建立時變Copula模型,結合蒙特卡洛模擬技術和擬合優(yōu)度檢驗,最終說明了從Kendall tau角度建立的時變Copula模型要優(yōu)于尾相關,基于Kendall tau的時變阿基米德Copula模型更能捕捉非線性的、非正態(tài)的時變相依機制,具有一定的優(yōu)越性。 (4)針對滬深股市的波動性以及非正態(tài)分布的非線性相依機制,結合Copula構造方法和建模技術,以t-Garch模型描述了滬深指數(shù)收益率的邊緣分布,采用擬合優(yōu)度檢驗選取最佳的Clayton Copula模型。 (5)根據(jù)建立的邊緣分布模型,并結合Clayton Copula模型,分別建立了單參數(shù)靜態(tài)Clayton Copula和時變Clayton Copula模型,計算對數(shù)收益率所對應的VaR值。 (6)將雙參數(shù)時變BB1Copula與風險價值相結合,建立雙參數(shù)時變Copula模型度量VaR,并對比了時變雙參數(shù)Copula模型與時變單參數(shù)Copula模型和傳統(tǒng)Copula模型在度量多資產(chǎn)組合風險的優(yōu)劣。
[Abstract]:The diversification of financial products, financial market instability and other reasons lead to the risk of financial assets is not controllable, with a very wide deep practical significance. Therefore research on risk value of portfolio risk due to the financial market is flexible, so it is the key problem of dynamic dependent mechanism, and time-varying Copula function can be dependent mechanism the variables in this paper. According to the characteristics of the financial market and the Copula modeling theory, starting from Copula to the Kendall tau dependent index tail correlation coefficient, the construction method and modeling technology together, build the time-varying Copula model successfully described, nonlinear, non normal time-varying dependent mechanism. Capture time-varying dependent mechanism including the tail:
(1) from the aspects of pattern recognition and model checking, we analyze the fine properties of Archimedes Copula model. Combined with the characteristics of financial data, we illustrate the feasibility, effectiveness and superiority of using time-varying Archimedes Copula model to analyze risks.
(2) starting from the one-to-one correspondence between Archimedes's Copula function and Kendall tau and tail correlation coefficient, we establish a Archimedes Copula model based on time-varying Kendall tau and time-varying tail correlation coefficient.
(3) Clayton Copula assumes that time-varying parameters for nonlinear AR (1) model, starting from the two angles of Kendall tau and the tail correlation coefficient, respectively establish time-varying Copula model. Combining with Monte Carlo simulation and test of goodness of fit, the establishment of the tau from the Kendall point of the time-varying Copula model is better than the tail, based on Kendall tau time-varying Archimedes Copula model can capture the nonlinear, non normal time-varying dependent mechanism, has certain advantages.
(4) in view of the volatility and non normal distribution mechanism of the Shanghai and Shenzhen stock markets, combined with the Copula construction method and modeling technology, we describe the marginal distribution of the Shanghai and Shenzhen stock index returns based on the t-Garch model, and select the best Clayton Copula model by goodness of fit test.
(5) according to the established edge distribution model and Clayton Copula model, single parameter static Clayton Copula and time-varying Clayton Copula models are established respectively, and the VaR value corresponding to logarithmic yield is calculated.
(6) combining the two parameter time-varying BB1Copula and the value at risk, we establish a two parameter time-varying Copula model to measure VaR. We compare the time varying two parameter Copula model with the time-varying single parameter Copula model and the traditional Copula model to measure the risk of multiple asset portfolios.

【學位授予單位】：西南交通大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：F224;F830.91

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