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Predictive Evaluation of Econometric Forecasting Models in Commodity Futures Markets

Author

Listed:
  • 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.

Suggested Citation

  • Zeng Tian & Swanson Norman R., 1998. "Predictive Evaluation of Econometric Forecasting Models in Commodity Futures Markets," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 2(4), pages 1-21, January.
  • Handle: RePEc:bpj:sndecm:v:2:y:1998:i:4:n:6
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    Cited by:

    1. Hendry, David F. & Martinez, Andrew B., 2017. "Evaluating multi-step system forecasts with relatively few forecast-error observations," International Journal of Forecasting, Elsevier, vol. 33(2), pages 359-372.
    2. Andrea BASTIANIN & Marzio GALEOTTI & Matteo MANERA, 2011. "Forecast evaluation in call centers: combined forecasts, flexible loss functions and economic criteria," Departmental Working Papers 2011-08, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    3. 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.
    4. Giliola Frey & Matteo Manera & Anil Markandya & Elisa Scarpa, 2009. "Econometric Models for Oil Price Forecasting: A Critical Survey," CESifo Forum, Ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 10(1), pages 29-44, April.
    5. 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.
    6. 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.
    7. Cheng, Gang & Yang, Yuhong, 2015. "Forecast combination with outlier protection," International Journal of Forecasting, Elsevier, vol. 31(2), pages 223-237.
    8. 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.
    9. Andrea Bastianin & Marzio Galeotti & Matteo Manera, 2011. "Forecast Evaluation in Call Centers: Combined Forecasts, Flexible Loss Functions and Economic Criteria," Working Papers 20110301, Università degli Studi di Milano-Bicocca, Dipartimento di Statistica.
    10. 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.
    11. Saporta, Victoria & Trott, Matt & Tudela, Merxe, 2009. "What can be said about the rise and fall in oil prices?," Bank of England Quarterly Bulletin, Bank of England, vol. 49(3), pages 215-225.
    12. Ai Han & Yanan He & Yongmiao Hong & Shouyang Wang, 2013. "Forecasting Interval-valued Crude Oil Prices via Autoregressive Conditional Interval Models," WISE Working Papers 2013-10-14, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.

    More about this item

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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