本文關(guān)鍵詞： Copula函數(shù) 金融市場(chǎng) 相關(guān)性 尾部相關(guān)　出處：《天津財(cái)經(jīng)大學(xué)》2014年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：隨著世界各國(guó)之間對(duì)外貿(mào)易的不斷加深以及對(duì)資本流動(dòng)、技術(shù)轉(zhuǎn)移和提供服務(wù)限制的逐漸放開(kāi),全球經(jīng)濟(jì)、金融市場(chǎng)由此形成了一個(gè)不可分割、相互影響的整體。全球金融市場(chǎng)之間的價(jià)格協(xié)同運(yùn)動(dòng)使得世界上任何國(guó)家金融市場(chǎng)的局部波動(dòng)都會(huì)快速的波及、傳染、放大到其他金融市場(chǎng),產(chǎn)生巨大的蝴蝶效應(yīng)。金融市場(chǎng)間的相關(guān)關(guān)系變得越來(lái)越復(fù)雜,多呈現(xiàn)非對(duì)稱(chēng)、非線性以及尾部相關(guān)的結(jié)構(gòu)形式。而Sklar提出的Copula函數(shù)可以捕捉到隨機(jī)變量間非線性的相關(guān)關(guān)系,同時(shí)Copula函數(shù)可以迅速有效地捕獲到非正態(tài)、非對(duì)稱(chēng)分布的尾部相關(guān)信息,在運(yùn)用Copula理論建立金融序列模型時(shí),還可將隨機(jī)變量的邊緣分布與它們之間的相關(guān)結(jié)構(gòu)分開(kāi)來(lái)研究,其中它們的相關(guān)結(jié)構(gòu)可由一個(gè)Copula函數(shù)來(lái)描述,這就大大簡(jiǎn)化了變量建模問(wèn)題。因此,運(yùn)用Copula理論研究金融市場(chǎng)間的相關(guān)性具有非常重要的理論意義和應(yīng)用價(jià)值。文章研究的重點(diǎn)包括三個(gè)部分：第一,對(duì)Copula函數(shù)的性質(zhì)及其函數(shù)族做了詳細(xì)的討論,在相關(guān)性的測(cè)度上引入了幾種直觀的圖形檢驗(yàn)方法,其中包括通過(guò)觀測(cè)樣本的秩數(shù)對(duì),判斷變量之間的相關(guān)關(guān)系以及在引入秩數(shù)對(duì)的基礎(chǔ)上提出了Chi-plot和K-plot檢驗(yàn)方法。第二,將半?yún)?shù)的估計(jì)方法與參數(shù)估價(jià)方法做了簡(jiǎn)要的對(duì)比,分析了二者的優(yōu)劣勢(shì)。同時(shí),介紹了一種在半?yún)?shù)估計(jì)方法下檢驗(yàn)?zāi)Ｐ蛿M合效果的方法,S。檢驗(yàn)方法。第三,采用參數(shù)估計(jì)方法和半?yún)?shù)估計(jì)方法實(shí)證分析了滬深指數(shù)收益率序列間的相關(guān)關(guān)系,通過(guò)擬合優(yōu)度檢驗(yàn)對(duì)多種Copulas函數(shù)進(jìn)行篩選,發(fā)現(xiàn)Gumbel Copula函數(shù)和t-Copula函數(shù)從整體上描述兩者相關(guān)結(jié)構(gòu)的能力較好,但為了更客觀反映二者之間的關(guān)系又構(gòu)造了M-Copula,結(jié)果表明其刻畫(huà)滬深股市間尾部相關(guān)性的效果更好。研究?jī)?nèi)容的創(chuàng)新點(diǎn)主要表現(xiàn)在以下兩個(gè)方面：第一,在相關(guān)性的測(cè)度上引入了幾種直觀的圖形檢驗(yàn)方法,使得應(yīng)用更加便捷。第二,實(shí)證分析了滬深指數(shù)收益率序列的相關(guān)結(jié)構(gòu)。
[Abstract]:With the deepening of foreign trade among countries in the world and the gradual liberalization of restrictions on capital flows, technology transfer and the provision of services, the global economy and financial markets have become inseparable. The price synergy between global financial markets makes the local volatility of financial markets in any country in the world quickly spread, spread, and magnify to other financial markets, The relationship between financial markets is becoming more and more complex, often in the form of asymmetric, nonlinear and tail dependent structure. Sklar's Copula function can capture the nonlinear correlation between random variables. At the same time, the Copula function can quickly and effectively capture the tail correlation information of non-normal and asymmetric distribution. When using Copula theory to establish the financial sequence model, the edge distribution of random variables and the correlation structure between them can be studied separately. Their related structures can be described by a Copula function, which greatly simplifies the problem of variable modeling. It is of great theoretical significance and practical value to use Copula theory to study the correlation between financial markets. The emphasis of this paper includes three parts: first, the properties of Copula functions and their families are discussed in detail. Several intuitionistic graphic test methods are introduced into the measure of correlation, including the rank pairs of observation samples, the correlation relationship between variables and the Chi-plot and K-plot test methods based on the introduction of rank number pairs. This paper makes a brief comparison between the semi-parameter estimation method and the parameter evaluation method, and analyzes their advantages and disadvantages. At the same time, a method to test the model fitting effect under the semi-parameter estimation method is introduced. By using parameter estimation method and semi-parameter estimation method, the correlation between Shanghai and Shenzhen index yield series is analyzed empirically, and various Copulas functions are screened by goodness of fit test. It is found that the Gumbel Copula function and the t-Copula function can describe the correlation structure of the two functions as a whole. But in order to reflect the relationship between the two more objectively and construct M-Copula, the result shows that it is better to depict the tail correlation between Shanghai and Shenzhen stock markets. The innovation of the research mainly shows in the following two aspects: first, Several intuitionistic graphic test methods are introduced to measure the correlation, which makes the application more convenient. Secondly, the correlation structure of the returns series of Shanghai and Shenzhen index is analyzed empirically.
【學(xué)位授予單位】：天津財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：F830.91;F224

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