Rate of Decay of the Tail Dependence Coefficient and Modeling for Bivariate Financial Returns Distributions
Thomas Fung1, Eugene Seneta2
1Department of Statistics, Macquarie University, Sydney, NSW, Australia; 2School of Mathematics and Statistics, University of Sydney, Sydney, NSW, Australia

Maximum likelihood estimates are obtained for long datasets of bivariate financial returns using mixing representation of the bivariate (skew) Variance Gamma and (skew) t distributions. By analysing simulated and real data, issues such as asymptotic lower tail dependence and competitiveness of the two models are illustrated. The index of regular variation in the rate of decay of the tail dependence coefficient helps explain apparent discrepancies.


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Finlay, R. and Seneta, E. (2008). Stationary-increment Variance-Gamma and t models: Simulation and parameter estimation. International StatisticalReview, 76, 167-186.

Fung, T. and Seneta, E. (2010). Modelling and Estimation for Bivariate Financial Returns. International Statistical Review, 78, 117-133.

Keywords: Variance Gamma distribution; Asymptotic tail dependence; Multivariate skew distribution

Biography: Thomas Fung is a recent Ph.D. graduate from the University of Sydney and currently a Lecturer in the Department of Statistics of the Macquarie University in Sydney. His area of interest is to study Variance Gamma (VG) distributions and their applications in the direction of competitiveness with other heavy tail distributions in the area of financial modelling. Currently, his research is focused on multivariate analysis and distribution theory in financial related areas.