Skew-t Copula-Based Semiparametric Markov Chains
Skew-t Copula-Based Semiparametric Markov Chains
Received:September 29, 2019  Revised:February 22, 2020
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中文关键词:  
英文关键词:Multivariate Markov chain process, copula, skew-t distribution
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Author NameAffiliationE-mail
Leming Qu Boise State University lqu@boisestate.edu 
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英文摘要:
      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.
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