Commodity futures and market efficiency: A fractional integrated approach
In financial time series, persistence or inertia is a feature usually observable in absolute returns, i.e., a proxy for volatility. Moreover, asset return series should be essentially unpredictable according to the efficiency market hypothesis (EMH) in its weak form. Surprisingly, recent literature has found evidence of anti-persistence in technology stocks and commodity futures returns. Anti-persistence would be indicative of an overreaction of asset prices to incoming information. In this article, we concentrate on a sample of 20 DJ-AIG commodity future indices--including broad indices and sub-indices (e.g., energy, grains, industrial metals, and livestock) over the period January 1991-June 2008. We conclude that returns series either over-react or under-react to new market information, which disconfirms the EMH in its weak form. Such disconfirmation would make it possible for market participants to devise non-linear statistical models for improved index forecasting and derivatives valuation.
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.
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Carbone, A. & Castelli, G. & Stanley, H.E., 2004. "Time-dependent Hurst exponent in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 344(1), pages 267-271.
- Fernandez, Viviana, 2007. "A postcard from the past: The behavior of U.S. stock markets during 1871–1938," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 386(1), pages 267-282.
- Mark J. Jensen, 1997.
"Using Wavelets to Obtain a Consistent Ordinary Least Squares Estimator of the Long Memory Parameter,"
- Jensen, Mark J, 1999. "Using wavelets to obtain a consistent ordinary least squares estimator of the long-memory parameter," MPRA Paper 39152, University Library of Munich, Germany.
- George Kapetanios, 2007. "Measuring Conditional Persistence in Nonlinear Time Series," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 69(3), pages 363-386, 06.
- Cornelis A. Los & Bing Yu, 2005.
"Persistence Characteristics of the Chinese Stock Markets,"
- Los, Cornelis A. & Yu, Bing, 2008. "Persistence characteristics of the Chinese stock markets," International Review of Financial Analysis, Elsevier, vol. 17(1), pages 64-82.
- Mielniczuk, J. & Wojdyllo, P., 2007. "Estimation of Hurst exponent revisited," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4510-4525, May.
- Thierry Ané & Loredana Ureche-Rangau, 2004.
"Does trading volume really explain stock returns volatility?,"
2004-FIN-02, IESEG School of Management.
- Ané, Thierry & Ureche-Rangau, Loredana, 2008. "Does trading volume really explain stock returns volatility?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 18(3), pages 216-235, July.
- Mulligan, Robert F., 2004. "Fractal analysis of highly volatile markets: an application to technology equities," The Quarterly Review of Economics and Finance, Elsevier, vol. 44(1), pages 155-179, February.
- Mark J. Jensen, 1997.
"An Alternative Maximum Likelihood Estimator of Long-Memeory Processes Using Compactly Supported Wavelets,"
- Jensen, Mark J., 2000. "An alternative maximum likelihood estimator of long-memory processes using compactly supported wavelets," Journal of Economic Dynamics and Control, Elsevier, vol. 24(3), pages 361-387, March.
- Christopher F. Baum & John Barkoulas, 2006.
"Long-memory forecasting of US monetary indices,"
Journal of Forecasting,
John Wiley & Sons, Ltd., vol. 25(4), pages 291-302.
- Mills, Terence C., 2004. "Statistical analysis of daily gold price data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 338(3), pages 559-566.
- John Barkoulas & Christopher Baum & Nickolaos Travlos, 2000.
"Long memory in the Greek stock market,"
Applied Financial Economics,
Taylor & Francis Journals, vol. 10(2), pages 177-184.
- Pasquini, Michele & Serva, Maurizio, 1999. "Multiscaling and clustering of volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 269(1), pages 140-147.
- Connor Jeff & Rossiter Rosemary, 2005. "Wavelet Transforms and Commodity Prices," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(1), pages 1-22, March.
When requesting a correction, please mention this item's handle: RePEc:eee:jrpoli:v:35:y:2010:i:4:p:276-282. 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 you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.