Assessing the stability of Gaussian mixture models for monthly returns of the S&P 500 index
AbstractThe study analyses the unconditional distribution of monthly S&P 500 stock index returns for the long-run time period 1871-2004. The return distribution can be adequately described by a mixture of two Gaussian normal distributions. However, when analysing sub-samples of this long-time horizon, substantial deviations between the empirical and the estimated two-component distribution become evident. Formal tests clearly reject the hypothesis of random draws from the estimated distribution. A comprehensive analysis of ten-year windows within the framework of a rolling window strategy reveals that window-specific estimated two-component mixtures can adequately describe the empirical distributions in almost all windows. Nevertheless, the substantial variation in the weight of the mixtures as well as in the parameters of the mixed distributions suggests that there are severe difficulties involved in maintaining the notion of an underlying distribution being constant to a certain degree.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by Taylor and Francis Journals in its journal Applied Financial Economics Letters.
Volume (Year): 3 (2007)
Issue (Month): 4 ()
Contact details of provider:
Web page: http://www.tandfonline.com/RAFL20
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statisticsgeneral 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: (Michael McNulty).
If references are entirely missing, you can add them using this form.