【摘要】：隨著金融全球化進(jìn)程的發(fā)展，各金融市場(chǎng)之間的相互依存性不斷加強(qiáng)，單一股票或市場(chǎng)的分析越來(lái)越不能滿足金融市場(chǎng)研究的需要，相關(guān)性分析在金融應(yīng)用中變得越來(lái)越重要，，已經(jīng)成為金融市場(chǎng)風(fēng)險(xiǎn)度量的關(guān)鍵。但是基于線性相關(guān)關(guān)系的傳統(tǒng)相關(guān)性度量只專注于線性相關(guān)的程度,忽視了金融市場(chǎng)結(jié)構(gòu)之間的關(guān)系,特別是尾部相關(guān)特征的研究。Granger因果分析方法只能做定性分析，無(wú)法給出定量的結(jié)論。本文將非線性相關(guān)分析工具——Copula方法用于金融市場(chǎng)的相關(guān)性研究中，分析了上證指數(shù)和恒生指數(shù)之間的常相關(guān)、時(shí)變相關(guān)的相關(guān)性和尾部相關(guān)性。 本文的內(nèi)容結(jié)構(gòu)安排如下： 第一部分介紹選題背景及意義、國(guó)內(nèi)外研究現(xiàn)狀；第二部分介紹關(guān)于Copula的一些基礎(chǔ)知識(shí)；第三部分介紹建模、參數(shù)估計(jì)和檢驗(yàn)；第四部分介紹本文用到的靜態(tài)和時(shí)變Copula函數(shù)；第五部分實(shí)證研究；第六部分為結(jié)論。 關(guān)于研究的方法,目前的研究多是假設(shè)邊緣分布服從t分布或正態(tài)分布,采用靜態(tài)Copula方法研究?jī)蓚�(gè)城市之間的相關(guān)性或尾部相關(guān)性,一般采用參數(shù)估計(jì)方法(Parametric Approach),參數(shù)估計(jì)方法一般會(huì)對(duì)邊緣分布函數(shù)作相關(guān)的假設(shè)（假定服從t分布或服從正態(tài)分布等）,但假設(shè)很難符合實(shí)際的情況,因此邊緣擬合效果往往不是很好,這往往影響參數(shù)估計(jì)效果。本文用基于秩的極大似然估計(jì)的方法,通過(guò)靜態(tài)C opula和時(shí)變C opula對(duì)滬港股票市場(chǎng)相關(guān)性及尾部相關(guān)性進(jìn)行研究。結(jié)果表明,滬港股市的相關(guān)性總體呈現(xiàn)增長(zhǎng)趨勢(shì)；尾部相關(guān)性方面，下尾相關(guān)程度略大于上尾，按兩階段分析，第一階段幾乎不存在下尾相關(guān)，上尾相關(guān)亦不明顯；第二階段的尾部（尤其是下尾）關(guān)聯(lián)程度明顯高于第一階段，并且下尾相關(guān)程度大于上尾。
[Abstract]:With the development of financial globalization and the interdependence of financial markets, the analysis of single stock or market can not meet the needs of financial market research, and correlation analysis is becoming more and more important in financial application. It has become the key to the measurement of financial market risk. However, the traditional correlation measurement based on linear correlation only focuses on the degree of linear correlation, neglecting the relationship between financial market structures, especially the tail correlation characteristics. Granger causality analysis method can only do qualitative analysis. It is impossible to give a quantitative conclusion. In this paper, Copula method, a nonlinear correlation analysis tool, is used to study the correlation between Shanghai Stock Exchange Index and Hang Seng Index, and the correlation between Shanghai Stock Exchange Index and Hang Seng Index, time-varying correlation and tail correlation are analyzed. The content structure of this paper is as follows: the first part introduces the background and significance of the topic, the research status at home and abroad; the second part introduces some basic knowledge about Copula; the third part introduces modeling, parameter estimation and test. The fourth part introduces the static and time-varying Copula functions used in this paper; the fifth part is empirical research; the sixth part is the conclusion. With regard to the research methods, most of the current studies assume that the edge distribution obeys t distribution or normal distribution. The static Copula method is used to study the correlation or tail correlation between the two cities, and the parameter estimation method (Parametric Approach), is generally used. The parameter estimation method generally makes relevant assumptions about the edge distribution function (assuming that it obeys t distribution or normal distribution, etc.), but it is difficult to conform to the actual situation, so the edge fitting effect is often not very good. This often affects the effect of parameter estimation. In this paper, the correlation and tail correlation of Shanghai and Hong Kong stock markets are studied by static C opula and time varying C opula by using the method of maximum likelihood estimation based on rank. The results show that the correlation of Shanghai and Hong Kong stock markets as a whole shows an increasing trend, and the correlation degree of the lower tail is slightly higher than that of the upper tail in terms of tail correlation. According to the two-stage analysis, there is almost no lower tail correlation in the first stage, and the upper tail correlation is not obvious. The correlation degree of the tail (especially the lower tail) of the second stage was significantly higher than that of the first stage, and the correlation degree of the lower tail was greater than that of the upper tail.
【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：F224;F832.51

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