Modelling and estimation for bivariate financial returns

Thomas Fung, Eugene Seneta*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)

Abstract

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
Volume78
Issue number1
DOIs
Publication statusPublished - Apr 2010
Externally publishedYes

Keywords

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

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