Advanced Search
MyIDEAS: Login to save this article or follow this journal

Predictive Evaluation of Econometric Forecasting Models in Commodity Futures Markets

Contents:

Author Info

  • Zeng Tian

    ()
    (Aeltus Investment Management, Inc.)

  • Swanson Norman R.

    ()
    (Pennsylvania State University University Park, Pennsylvania, USA)

Abstract

The predictive accuracy of various econometric models, including random walks, vector-autoregressive and vector-error-correction models, are investigated using daily futures prices of four commodities (the S&P 500 index, treasury bonds, gold, and crude oil). All models are estimated using a rolling-window approach, and evaluated by both in-sample and out-of-sample performance measures. The criteria considered include system criteria, where we evaluate multiequation forecasting models, and univariate forecast-accuracy criteria. The five univariate criteria are root mean square error (RMSE), mean absolute deviation (MAD), mean absolute percentage error (MAPE), confusion matrix (CM), and confusion rate (CR). The five system criteria used include the trace of second-moment matrix of the forecast-errors matrix (TMSE), the trace of second-moment matrix of percentage-forecast errors (TMAPE), the generalized forecast-error second-moment matrix (GFESM), and a trading-rule profit criterion (TPC) based on a maximum-spread trading strategy. An in-sample criterion, the mean Schwarz information criteria (MSIC), is also computed. Our results suggest that error-correction models perform better in shorter forecast horizons, when models are compared based on quadratic loss measures and confusion matrices. However, the error-correction models which we consider perform better at all forecast horizons (one to five steps ahead) when models are compared based on a profit-maximization loss function. Further, our error-correction model, where the error-correction term is constructed according to a cost-of-carry equilibrium condition, outperforms our alternative error-correction model, which uses the price spreads as the error-correction term.

Download Info

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.
File URL: http://www.degruyter.com/view/j/snde.1998.2.4/snde.1998.2.4.1037/snde.1998.2.4.1037.xml?format=INT
Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.

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.

Bibliographic Info

Article provided by De Gruyter in its journal Studies in Nonlinear Dynamics & Econometrics.

Volume (Year): 2 (1998)
Issue (Month): 4 (January)
Pages: 1-21

as in new window
Handle: RePEc:bpj:sndecm:v:2:y:1998:i:4:n:6

Contact details of provider:
Web page: http://www.degruyter.com

Order Information:
Web: http://www.degruyter.com/view/j/snde

Related research

Keywords:

Other versions of this item:

Find related papers by JEL classification:

References

No references listed on IDEAS
You can help add them by filling out this form.

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as in new window

Cited by:
  1. Andrea Bastianin & Matteo Manera & Anil Markandya & Elisa Scarpa, 2011. "Oil Price Forecast Evaluation with Flexible Loss Functions," Working Papers 2011.91, Fondazione Eni Enrico Mattei.
  2. Matteo Manera & Chiara Longo & Anil Markandya & Elisa Scarpa, 2007. "Evaluating the Empirical Performance of Alternative Econometric Models for Oil Price Forecasting," Working Papers 2007.4, Fondazione Eni Enrico Mattei.
  3. Giliola Frey & Matteo Manera & Anil Markandya & Elisa Scarpa, 2009. "Econometric Models for Oil Price Forecasting: A Critical Survey," CESifo Forum, Ifo Institute for Economic Research at the University of Munich, vol. 10(1), pages 29-44, 04.
  4. Van Bellegem, Sebastien & von Sachs, Rainer, 2004. "Forecasting economic time series with unconditional time-varying variance," International Journal of Forecasting, Elsevier, vol. 20(4), pages 611-627.
  5. Claudio Dicembrino & Pasquale Lucio Scandizzo, 2012. "The Fundamental and Speculative Components of the Oil Spot Price: A Real Option Value Approach," CEIS Research Paper 229, Tor Vergata University, CEIS, revised 18 Apr 2012.
  6. Batchelor, Roy & Alizadeh, Amir & Visvikis, Ilias, 2007. "Forecasting spot and forward prices in the international freight market," International Journal of Forecasting, Elsevier, vol. 23(1), pages 101-114.

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:bpj:sndecm:v:2:y:1998:i:4:n:6. 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: (Peter Golla).

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.