Modelling and estimation for bivariate financial returns

Thomas Fung, Eugene Seneta*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


Maximum likelihood estimates are obtained for long data sets of bivariate financial returns using mixing representation of the bivariate (skew) Variance Gamma (VG) and two (skew) t distributions. By analysing simulated and real data, issues such as asymptotic lower tail dependence and competitiveness of the three models are illustrated. A brief review of the properties of the models is included. The present paper is a companion to papers in this journal by Demarta & McNeil and Finlay & Seneta.

Original languageEnglish
Pages (from-to)117-133
Number of pages17
JournalInternational Statistical Review
Issue number1
Publication statusPublished - Apr 2010
Externally publishedYes


  • Asymptotic tail dependence
  • Maximum likelihood estimation
  • Multivariate skew distribution
  • Skew t distributions
  • Skew Variance Gamma distribution
  • Tail behaviour
  • WinBUGS


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