本文關(guān)鍵詞：Pair-Copula自回歸模型及其在股票指數(shù)中的應(yīng)用　出處：《吉林大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： Pair-Copula Construction 藤 COPAR

【摘要】：Copula不受邊緣分布選擇的限制,可以靈活地構(gòu)造多元分布,被應(yīng)用在越來越多的領(lǐng)域中.本文回顧和總結(jié)了與Copula相關(guān)的基礎(chǔ)理論,引入了藤結(jié)構(gòu)Copula模型.特別地,詳細地介紹了以C藤和D藤為代表的藤結(jié)構(gòu)Copula模型.在分解多元函數(shù)時,藤為二元Copula的選擇提供了一定的依據(jù).利用Pair-Copula構(gòu)造多元變量的密度函數(shù)時,多元數(shù)據(jù)間復(fù)雜的相關(guān)性可以通過二元Copula刻畫出來.在金融和經(jīng)濟等領(lǐng)域中,對于多元時間序列的分析是一種常見的問題,傳統(tǒng)的分析工具是向量自回歸(Vector Autoregreesion,簡稱VAR)模型,但VAR模型僅限于反映序列間線性相關(guān)和對稱相關(guān)性.而接下來介紹的基于D藤定義的Copula自回歸(Copula Autoregression,簡稱COPAR)模型能夠更好的刻畫多元時間序列間的相關(guān)性,尤其是在研究一個時間序列對于另一個時間序列的影響時非常有效,并且給出了相應(yīng)的Copula矩陣以及拓展到高維的方法.在這個模型的基礎(chǔ)上,我們提出了一個基于C藤的新的COPAR模型,該模型也能夠拓展到任意維且能夠預(yù)測未來值.文章的最后利用Copula基礎(chǔ)理論來研究美國與亞洲、澳洲和歐洲主要國家股票指數(shù)同比變化率,并利用R軟件中的函數(shù)分析數(shù)據(jù)關(guān)系、選擇二元Copula和估計相關(guān)Copula參數(shù),得出美國與各國之間股票指數(shù)同比變化率的相關(guān)關(guān)系。
[Abstract]:Copula can construct multivariate distribution flexibly and can be used in more and more fields. The basic theories related to Copula are reviewed and summarized in this paper. In this paper, Copula model of rattan structure is introduced. In particular, the Copula model of rattan structure, represented by C and D vines, is introduced in detail. Rattan provides a basis for the selection of binary Copula. Using Pair-Copula to construct the density function of multivariate variables. The complex correlation between multivariate data can be described by binary Copula. In the fields of finance and economy, the analysis of multivariate time series is a common problem. The traditional analysis tool is vector autoregressive vector autoregressive (VAR) model. But the VAR model is limited to reflect the linear correlation and symmetric correlation between sequences. Then the Copula autoregressive model based on the definition of D-rattan is presented. Copula Autoregression. COPAR) model can better describe the correlation between multivariate time series, especially in the study of the influence of one time series on another time series is very effective. The corresponding Copula matrix and the method to extend to high dimension are given. On the basis of this model, we propose a new COPAR model based on C-tene. The model can also be extended to arbitrary dimensions and can predict future values. Finally, the paper uses Copula basic theory to study the rate of stock index change in major countries of the United States and Asia, Australia and Europe. Using the function in R software to analyze the data relationship, select binary Copula and estimate the relevant Copula parameters, and obtain the correlation relationship between the stock index change rate of the United States and other countries.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：F224;F831.51

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本文編號：1388003