A simple graphical method to explore tail-dependence in stock-return pairs

Klaus Abberger

No 04/03, CoFE Discussion Papers from University of Konstanz, Center of Finance and Econometrics (CoFE)

Abstract: For a bivariate data set the dependence structure can not only be measured globally, for example with the Bravais-Pearson correlation coefficient, but the dependence structure can also be analyzed locally. In this article the exploration of dependencies in the tails of the bivariate distribution is discussed. For this a graphical method which is called chi-plot and which was introduced by Fisher and Switzer (1985, 2001) is used. Examples with simulated data sets illustrate that the chi-plot is suitable for the exploration of dependencies. This graphical method is then used to examine stock-return pairs. The kind of tail-dependence between returns has consequences, for example, for the calculation of the Value at Risk and should be modelled carefully. The application of the chi-plot to various daily stock-return pairs shows that different dependence structures can be found. This graph can therefore be an interesting aid for the modelling of returns.

Keywords: Association; bivariate distribution; chi-plot; copula; correlation; local dependence; tail-dependence (search for similar items in EconPapers)
Date: 2004
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.econstor.eu/bitstream/10419/23565/1/dp04_03.pdf (application/pdf)

Related works:
Journal Article: A simple graphical method to explore tail-dependence in stock-return pairs (2005)
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:zbw:cofedp:0403

Access Statistics for this paper

More papers in CoFE Discussion Papers from University of Konstanz, Center of Finance and Econometrics (CoFE) Contact information at EDIRC.
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().