本文選題：Kendall　切入點(diǎn)：tau　出處：《西南交通大學(xué)》2013年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：金融產(chǎn)品的多樣化,金融市場(chǎng)的不穩(wěn)定性等原因?qū)е陆鹑谫Y產(chǎn)風(fēng)險(xiǎn)的不可控,因此研究風(fēng)險(xiǎn)價(jià)值具有很廣很深的實(shí)際意義。多資產(chǎn)組合風(fēng)險(xiǎn)因金融市場(chǎng)的靈活多樣,故其關(guān)鍵問(wèn)題是動(dòng)態(tài)相依機(jī)制的刻畫(huà),而時(shí)變Copula函數(shù)能夠很好的刻畫(huà)變量間的相依機(jī)制。本文根據(jù)金融市場(chǎng)特征以及Copula建模理論,從Copula所導(dǎo)致的相依指標(biāo)Kendall tau尾相關(guān)系數(shù)出發(fā),將構(gòu)造方法和建模技術(shù)結(jié)合在一起,建立了時(shí)變Copula模型,成功描述了非線性的、非正態(tài)的時(shí)變相依機(jī)制,成功捕捉了下尾相依機(jī)制的時(shí)變性。主要工作包括： (1)從模式的識(shí)別、模型的檢驗(yàn)等方面分析了阿基米德Copula模型的優(yōu)良性質(zhì),并結(jié)合金融數(shù)據(jù)的特性,說(shuō)明了利用時(shí)變阿基米德Copula模型分析風(fēng)險(xiǎn)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(guān)系數(shù)之間的一一對(duì)應(yīng)關(guān)系出發(fā),建立基于時(shí)變Kendall tau、時(shí)變尾相關(guān)系數(shù)的阿基米德Copula模型。 (3)假定Clayton Copula的時(shí)變參數(shù)為非線性的AR(1)模型,從Kendall tau和尾相關(guān)系數(shù)兩個(gè)角度出發(fā),分別建立時(shí)變Copula模型,結(jié)合蒙特卡洛模擬技術(shù)和擬合優(yōu)度檢驗(yàn),最終說(shuō)明了從Kendall tau角度建立的時(shí)變Copula模型要優(yōu)于尾相關(guān),基于Kendall tau的時(shí)變阿基米德Copula模型更能捕捉非線性的、非正態(tài)的時(shí)變相依機(jī)制,具有一定的優(yōu)越性。 (4)針對(duì)滬深股市的波動(dòng)性以及非正態(tài)分布的非線性相依機(jī)制,結(jié)合Copula構(gòu)造方法和建模技術(shù),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(yàn)選取最佳的Clayton Copula模型。 (5)根據(jù)建立的邊緣分布模型,并結(jié)合Clayton Copula模型,分別建立了單參數(shù)靜態(tài)Clayton Copula和時(shí)變Clayton Copula模型,計(jì)算對(duì)數(shù)收益率所對(duì)應(yīng)的VaR值。 (6)將雙參數(shù)時(shí)變BB1Copula與風(fēng)險(xiǎn)價(jià)值相結(jié)合,建立雙參數(shù)時(shí)變Copula模型度量VaR,并對(duì)比了時(shí)變雙參數(shù)Copula模型與時(shí)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(fēng)險(xiǎn)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(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類(lèi)號(hào)】：F224;F830.91

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 馬超群,李紅權(quán);VaR方法及其在金融風(fēng)險(xiǎn)管理中的應(yīng)用[J];系統(tǒng)工程;2000年02期

2 陳學(xué)華,楊輝耀;股市風(fēng)險(xiǎn)VaR與ES的動(dòng)態(tài)度量與分析[J];系統(tǒng)工程;2004年01期

3 楊湘豫;周再立;;基于Copula-TARCH的開(kāi)放式基金投資組合風(fēng)險(xiǎn)的實(shí)證分析[J];系統(tǒng)工程;2011年06期

4 鄭文通;金融風(fēng)險(xiǎn)管理的VAR方法及其應(yīng)用[J];國(guó)際金融研究;1997年09期

5 王春峰,李剛;基于分布擬合法的VaR估計(jì)[J];管理工程學(xué)報(bào);2002年04期

6 陳守東,王魯非;上證綜合指數(shù)VaR的度量[J];數(shù)量經(jīng)濟(jì)技術(shù)經(jīng)濟(jì)研究;2002年04期

7 胡經(jīng)生,王榮,丁成;VaR方法及其拓展模型在投資組合風(fēng)險(xiǎn)管理中的應(yīng)用研究[J];數(shù)量經(jīng)濟(jì)技術(shù)經(jīng)濟(jì)研究;2005年05期

8 柏滿迎;孫祿杰;;三種Copula-VaR計(jì)算方法與傳統(tǒng)VaR方法的比較[J];數(shù)量經(jīng)濟(jì)技術(shù)經(jīng)濟(jì)研究;2007年02期

9 傅強(qiáng);彭選華;;基于MCMC算法的時(shí)變Copula-GARCH-t模型參數(shù)估計(jì)及應(yīng)用[J];數(shù)量經(jīng)濟(jì)技術(shù)經(jīng)濟(jì)研究;2011年07期

10 朱世武;;基于Copula的VsR度量與事后檢驗(yàn)[J];數(shù)理統(tǒng)計(jì)與管理;2007年06期

相關(guān)博士學(xué)位論文 前1條

1 林小明;險(xiǎn)值理論及其應(yīng)用研究[D];廈門(mén)大學(xué);2001年

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