Nonparametric modeling of carbon prices
This paper constitutes the first exercise of nonparametric modeling applied to carbon markets. The framework of analysis is carefully detailed, and the empirical application unfolds in the case of BlueNext spot and ECX futures prices. The data is gathered in daily frequency from April 2005 to April 2010. First, we document the presence of strong nonlinearities in the conditional mean functions. Second, the conditional volatility functions reveal an asymmetric and heteroskedastic behavior which is dramatically different between carbon spot and futures logreturns. The results for spot prices are also robust to subsamples' decomposition. Third, we show in an out-of-sample forecasting exercise that nonparametric modeling allows reducing the prediction error by almost 15% compared to linear AR models. This latter result is confirmed by the Diebold–Mariano pairwise test statistic.
If 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.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
When requesting a correction, please mention this item's handle: RePEc:eee:eneeco:v:33:y:2011:i:6:p:1267-1282. 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: (Zhang, Lei)
If references are entirely missing, you can add them using this form.