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A data envelopment analysis-based framework for the relative performance evaluation of competing crude oil prices' volatility forecasting models

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  • Xu, Bing
  • Ouenniche, Jamal

Abstract

Forecasts of crude oil prices' volatility are important inputs to many decision making processes in application areas such as macroeconomic policy making, risk management, options pricing, and portfolio management. Despite the fact that a large number of forecasting models have been designed to forecast crude oil prices' volatility, so far the relative performance evaluation of competing forecasting models remains an exercise that is unidimensional in nature. To be more specific, most studies tend to use several criteria and their measures to assess the relative performance of these models, but competing models are always ranked by performance measure; thus, leading in general to different rankings for different criteria and to a situation where one cannot make an informed decision as to which model performs best with respect to all criteria under consideration. The purpose of this paper is to propose a single ranking that takes account of several criteria using a Data Envelopment Analysis framework. Our empirical results reveal that the unidimensional rankings for different criteria might differ significantly and that the multidimensional ranking of some models could be substantially different from their unidimensional rankings, which highlights the importance of the proposed performance evaluation tool.

Suggested Citation

  • Xu, Bing & Ouenniche, Jamal, 2012. "A data envelopment analysis-based framework for the relative performance evaluation of competing crude oil prices' volatility forecasting models," Energy Economics, Elsevier, vol. 34(2), pages 576-583.
  • Handle: RePEc:eee:eneeco:v:34:y:2012:i:2:p:576-583 DOI: 10.1016/j.eneco.2011.12.005
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    References listed on IDEAS

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    Cited by:

    1. Sueyoshi, Toshiyuki & Yuan, Yan & Goto, Mika, 2017. "A literature study for DEA applied to energy and environment," Energy Economics, Elsevier, pages 104-124.
    2. Sueyoshi, Toshiyuki & Goto, Mika, 2012. "Returns to scale and damages to scale on U.S. fossil fuel power plants: Radial and non-radial approaches for DEA environmental assessment," Energy Economics, Elsevier, pages 2240-2259.
    3. Xiong, Tao & Bao, Yukun & Hu, Zhongyi, 2013. "Beyond one-step-ahead forecasting: Evaluation of alternative multi-step-ahead forecasting models for crude oil prices," Energy Economics, Elsevier, pages 405-415.
    4. Nuri Ozgur DOGAN & Can Tansel TUGCU, 2015. "Energy Efficiency in Electricity Production: A Data Envelopment Analysis (DEA) Approach for the G-20 Countries," International Journal of Energy Economics and Policy, Econjournals, pages 246-252.
    5. repec:eee:eneeco:v:67:y:2017:i:c:p:315-327 is not listed on IDEAS
    6. Opeyemi Akinyemi & Philip O. Alege & Oluseyi O. Ajayi & Lloyd Amaghionyeodiwe & Adeyemi A. Ogundipe, 2015. "Fuel Subsidy Reform and Environmental Quality in Nigeria," International Journal of Energy Economics and Policy, Econjournals, pages 540-549.
    7. repec:eee:jrpoli:v:54:y:2017:i:c:p:58-70 is not listed on IDEAS
    8. Wen, Fenghua & Gong, Xu & Cai, Shenghua, 2016. "Forecasting the volatility of crude oil futures using HAR-type models with structural breaks," Energy Economics, Elsevier, pages 400-413.

    More about this item

    Keywords

    Forecasting; Performance criteria; Performance evaluation; Volatility; GARCH; Data Envelopment Analysis (DEA);

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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