A general methodology for time series modelling is developed which works down from distributional\ud properties to implied structural models including the standard regression relationship. This\ud general to specific approach is important since it can avoid spurious assumptions such as linearity\ud in the form of the dynamic relationship between variables. It is based on splitting the multivariate\ud distribution of a time series into two parts: (i) the marginal unconditional distribution, (ii) the\ud serial dependence encompassed in a general function , the copula. General properties of the class of\ud copula functions that fulfill the necessary requirements for Markov chain construction are exposed.\ud Special cases for the gaussian copula with AR(p) dependence structure and for archimedean copulae\ud are presented. We also develop copula based dynamic dependency measures — auto-concordance\ud in place of autocorrelation. Finally, we provide empirical applications using financial returns and\ud transactions based forex data. Our model encompasses the AR(p) model and allows non-linearity.\ud Moreover, we introduce non-linear time dependence functions that generalize the autocorrelation\ud function
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