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Further Study of the DEA-Based Framework for Performance Evaluation of Competing Crude Oil Prices’ Volatility Forecasting Models

Author

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  • Zhongbao Zhou

    (School of Business Administration, Hunan University, Changsha 410082, China)

  • Qianying Jin

    (School of Business Administration, Hunan University, Changsha 410082, China)

  • Jian Peng

    (School of Business Administration, Hunan University, Changsha 410082, China)

  • Helu Xiao

    (School of Business, Hunan Normal University, Changsha 410081, China)

  • Shijian Wu

    (College of Economics and Management, Shandong University of Science and Technology, Qingdao 266590, China)

Abstract

The super-efficiency data envelopment analysis model is innovative in evaluating the performance of crude oil prices’ volatility forecasting models. This multidimensional ranking, which takes account of multiple criteria, gives rise to a unified decision as to which model performs best. However, the rankings are unreliable because some efficiency scores are infeasible solutions in nature. What’s more, the desirability of indexes is worth discussing so as to avoid incorrect rankings. Hence, herein we introduce four models, which address the issue of undesirable characteristics of indexes and infeasibility of the super efficiency models. The empirical results reveal that the new rankings are more robust and quite different from the existing results.

Suggested Citation

  • Zhongbao Zhou & Qianying Jin & Jian Peng & Helu Xiao & Shijian Wu, 2019. "Further Study of the DEA-Based Framework for Performance Evaluation of Competing Crude Oil Prices’ Volatility Forecasting Models," Mathematics, MDPI, vol. 7(9), pages 1-10, September.
    • Handle: RePEc:gam:jmathe:v:7:y:2019:i:9:p:827-:d:264820
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    References listed on IDEAS

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    Cited by:

    1. Arthur J. Lin & Hai-Yen Chang, 2020. "Volatility Transmission from Equity, Bulk Shipping, and Commodity Markets to Oil ETF and Energy Fund—A GARCH-MIDAS Model," Mathematics, MDPI, vol. 8(9), pages 1-21, September.
    2. Tai-Shan Lee & Ching-Hsin Wang & Chun-Min Yu, 2019. "Fuzzy Evaluation Model for Enhancing E-Learning Systems," Mathematics, MDPI, vol. 7(10), pages 1-11, October.

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