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

Klaus Abberger ()
Additional contact information
Klaus Abberger: IFO Munich

No 04-03, CoFE Discussion Paper from Center of Finance and Econometrics, University of Konstanz

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)
New Economics Papers: this item is included in nep-ecm and nep-fmk
Date: Written 2004-02
View list of references

Downloads: (external link)
http://cofe.uni-konstanz.de/Papers/dp04_03.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Ordering information: This working paper can be ordered from
http://cofe.uni-konstanz.de

Access Statistics for this paper

More papers in CoFE Discussion Paper from Center of Finance and Econometrics, University of Konstanz
Contact information at EDIRC.
Series data maintained by Ingmar Nolte ().