本文選題：極端事件　切入點：混合Copula　出處：《蘇州大學》2016年碩士論文

【摘要】：極端事件是指很少發(fā)生,然而一旦發(fā)生卻產(chǎn)生極大影響的事件,因此關于極端事件的研究具有重要的實際意義。與單一的Copula函數(shù)相比,混合Copula函數(shù)可以通過自由選擇不同類型的Copula函數(shù)來建立相關結構,能更準確地刻畫極端事件發(fā)生的復雜關系。利用Gumbel Copula、Clayton Copula和Frank Copula構建的混合Copula函數(shù),既可以涵蓋單一上尾或下尾存在的情形,還可以展現(xiàn)上尾和下尾同時存在的情形。本文首次利用基于混合Copula函數(shù)的極端事件Logistic回歸模型,將極端事件的影響因素和關聯(lián)事件同時加入到模型中,可以利用條件概率去估計極端事件發(fā)生的概率。該模型充分利用了Copula函數(shù)在尾部相關性和Logistic回歸在處理分類變量上的優(yōu)勢,優(yōu)化了極端事件的概率估計和擬合精度。
[Abstract]:Extreme events refer to events that occur rarely, but once they occur, they have a great impact. Therefore, the study of extreme events has important practical significance.Compared with a single Copula function, the hybrid Copula function can establish the correlation structure by freely selecting different types of Copula functions, and can describe the complex relationship of extreme events more accurately.The hybrid Copula function constructed by Gumbel Copula Clayton Copula and Frank Copula can not only cover the existence of single upper tail or lower tail, but also show the existence of upper tail and lower tail.In this paper, the Logistic regression model of extreme events based on mixed Copula function is used for the first time. The influence factors of extreme events and the associated events are added to the model at the same time. The conditional probability can be used to estimate the probability of extreme events.The model makes full use of the advantages of Copula function in tail correlation and Logistic regression in dealing with classification variables, and optimizes the probability estimation and fitting accuracy of extreme events.
【學位授予單位】：蘇州大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：F224

【相似文獻】

相關期刊論文 前10條

1 孫志賓;;混合Copula模型在中國股市的應用[J];數(shù)學的實踐與認識;2007年20期

2 李娟;戴洪德;劉全輝;;幾種Copula函數(shù)在滬深股市相關性建模中的應用[J];數(shù)學的實踐與認識;2007年24期

3 李軍;;Copula-EVT Based Tail Dependence Structure of Financial Markets in China[J];Journal of Southwest Jiaotong University(English Edition);2008年01期

4 許建國;杜子平;;非參數(shù)Bernstein Copula理論及其相關性研究[J];工業(yè)技術經(jīng)濟;2009年04期

5 王s，

本文編號：1705730