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