Skew-t Copula-Based Semiparametric Markov Chains  

作　　者：Leming QU 

机构地区：[1]Boise State University,Boise,ID,USA

出　　处：《Journal of Mathematical Research with Applications》2020年第6期647-658,共12页数学研究及应用（英文版）

摘　　要：Without specifying the structure of a time series,we model the distribution of a multivariate Markov process in discrete time by the corresponding multivariate Markov family and the one-dimensional flows of marginal distributions.Such models tackle simultaneously temporal dependence and contemporaneous dependence between time series.A specific parametric form of stationary copula,namely skew-t copula,is assumed.Skew-t copulas are capable of modeling asymmetry,skewness,and heavy tails.An empirical study with unfiltered daily returns for three stock indices shows that the skew-t copula Markov model provides a better fit than the skew-Normal copula Markov or t-copula Markov model,and the skew-t copula model without Markov property.

关 键 词：multivariate Markov chain process COPULA skew-t distribution 

分 类 号：O211.62[理学—概率论与数理统计]