Modelling and estimation for bivariate financial returns

Thomas Fung; Eugene Seneta

doi:10.1111/j.1751-5823.2010.00106.x

Modelling and estimation for bivariate financial returns

Thomas Fung, Eugene Seneta^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

16 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 language	English
Pages (from-to)	117-133
Number of pages	17
Journal	International Statistical Review
Volume	78
Issue number	1
DOIs	https://doi.org/10.1111/j.1751-5823.2010.00106.x
Publication status	Published - Apr 2010
Externally published	Yes

Keywords

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

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10.1111/j.1751-5823.2010.00106.x

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KW - Multivariate skew distribution

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KW - Skew Variance Gamma distribution

KW - Tail behaviour

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Modelling and estimation for bivariate financial returns

Abstract

Keywords

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