Clustering financial time series with variance ratio statistics
This study introduces a new distance measure for clustering financial time series based on variance ratio test statistics. The proposed metric attempts to assess the level of interdependence of time series from the point of view of return predictability. Simulation results show that this metric aggregates better time series according to their serial dependence structure than a metric based on the sample autocorrelations. An empirical application of this approach to international stock market returns is presented. The results suggest that this metric discriminates reasonably well stock markets according to size and level of development. Furthermore, despite the substantial evolution of individual variance ratio statistics, the clustering pattern remains fairly stable across different time periods.
|Date of creation:||Sep 2009|
|Date of revision:|
|Contact details of provider:|| Postal: |
Web page: http://cemapre.iseg.utl.pt/
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:cma:wpaper:0904. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Helena Lima)
If references are entirely missing, you can add them using this form.