统计与信息论坛
統計與信息論罈
통계여신식론단
STATISTICS & INFORMATION TRIBUNE
2014年
6期
34-39,40
,共7页
分层阿基米德copula%两阶段极大似然法%ARMA-GARCH过程%金融时间序列
分層阿基米德copula%兩階段極大似然法%ARMA-GARCH過程%金融時間序列
분층아기미덕copula%량계단겁대사연법%ARMA-GARCH과정%금융시간서렬
hierarchical Archimedean Copula%2-step maximum likelihood method%ARMA -GARCH process%financial time series
与阿基米德copula相比，分层阿基米德copula（HAC）的结构更具一般性，而相比于椭圆型copula它的待估参数个数更少。用两阶段极大似然法来估计HAC函数，主要的步骤是先估计出每个分量的边际分布，以此为基础再估计copula函数。实证分析中，采取Clayton和Gumbel型的HAC分析四只股票价格序列之间的相关性。在得出 HAC的结构和估计其参数之前，运用ARMA -GARCH过程消除了序列的自相关性和条件异方差。通过比较赤迟信息准则，认为完全嵌套的Gumbel型HAC能更好地刻画这种相关性。
與阿基米德copula相比，分層阿基米德copula（HAC）的結構更具一般性，而相比于橢圓型copula它的待估參數箇數更少。用兩階段極大似然法來估計HAC函數，主要的步驟是先估計齣每箇分量的邊際分佈，以此為基礎再估計copula函數。實證分析中，採取Clayton和Gumbel型的HAC分析四隻股票價格序列之間的相關性。在得齣 HAC的結構和估計其參數之前，運用ARMA -GARCH過程消除瞭序列的自相關性和條件異方差。通過比較赤遲信息準則，認為完全嵌套的Gumbel型HAC能更好地刻畫這種相關性。
여아기미덕copula상비，분층아기미덕copula（HAC）적결구경구일반성，이상비우타원형copula타적대고삼수개수경소。용량계단겁대사연법래고계HAC함수，주요적보취시선고계출매개분량적변제분포，이차위기출재고계copula함수。실증분석중，채취Clayton화Gumbel형적HAC분석사지고표개격서렬지간적상관성。재득출 HAC적결구화고계기삼수지전，운용ARMA -GARCH과정소제료서렬적자상관성화조건이방차。통과비교적지신식준칙，인위완전감투적Gumbel형HAC능경호지각화저충상관성。
In this paper ,we introduce the hierarchical Archimedean Copula (HAC) which is more flexible compared with the simple Archimedean Copula , and require a smaller number of parameters compared to elliptical copula .The 2 -step maximum likelihood method is discussed which estimates the marginal distribution functions and Copula function ,separately .For empirical study ,we apply HAC with Clayton and Gumbel generators for modelling the dependence of four stocks ,respectively .The ARMA -GARCH process is used to model the series correlation and the conditional heteroscadesticity in each financial time series .The best structure and the estimation of the parameters of HAC are also received .In summary ,based on Akaike information criterion ,we conclude that the fully nested HAC with Gumbel generator exhibits better performance in this case .
