Bouyé, Eric, Gaussel, Nicolas and Salmon, Mark H. (Mark Howard), 1949- (2001) Investigating dynamic dependence using copulae. Working Paper. Warwick Business School Financial Econometrics Research Centre, University of Warwick.

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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.

Item Type:	Working or Discussion Paper (Working Paper)
Subjects:	Q Science > QA Mathematics
Divisions:	Faculty of Social Sciences > Warwick Business School > Financial Econometrics Research Centre Faculty of Social Sciences > Warwick Business School
Library of Congress Subject Headings (LCSH):	Time-series analysis, Copulas (Mathematical statistics), Econometrics
Series Name:	Working Papers Series
Publisher:	Warwick Business School Financial Econometrics Research Centre
Place of Publication:	University of Warwick
Date:	2001
Volume:	Vol.2001
Number:	No.3
Status:	Not Peer Reviewed
Access rights to Published version:	Open Access
Funder:	Economic and Social Research Council (Great Britain) (ESRC)
Grant number:	nb0R00429834305 (ESRC)
URI:	http://wrap.warwick.ac.uk/id/eprint/1824

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