Testing the Gaussian copula hypothesis for financial assets dependences

Yannick Malevergne and D. Sornette

Quantitative Finance, 2003, vol. 3, issue 4, 231-250

Abstract: Using one of the key properties of copulas that they remain invariant under an arbitrary monotonic change of variable, we investigate the null hypothesis that the dependence between financial assets can be modelled by the Gaussian copula. We find that most pairs of currencies and pairs of major stocks are compatible with the Gaussian copula hypothesis, while this hypothesis can be rejected for the dependence between pairs of commodities (metals). Notwithstanding the apparent qualification of the Gaussian copula hypothesis for most of the currencies and the stocks, a non-Gaussian copula, such as the Student copula, cannot be rejected if it has sufficiently many 'degrees of freedom'. As a consequence, it may be very dangerous to embrace blindly the Gaussian copula hypothesis, especially when the coefficient of correlation between the pairs of assets is too high, such that the tail dependence neglected by the Gaussian copula can became large, leading to the ignoring of extreme events which may occur in unison.

Date: 2003
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (66)

Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1088/1469-7688/3/4/301 (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Testing the Gaussian copula hypothesis for financial assets dependences (2003)
Working Paper: Testing the Gaussian copula hypothesis for financial assets dependence (2003)
Working Paper: Testing the Gaussian Copula Hypothesis for Financial Assets Dependences (2001)
Working Paper: Testing the Gaussian Copula Hypothesis for Financial Assets Dependences (2001)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:quantf:v:3:y:2003:i:4:p:231-250

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RQUF20

DOI: 10.1088/1469-7688/3/4/301

Access Statistics for this article

Quantitative Finance is currently edited by Michael Dempster and Jim Gatheral

More articles in Quantitative Finance from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().