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

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.

[1]  Ioannis Giannikos,et al.  A modeling framework for incorporating DEA efficiency into set covering, packing, and partitioning formulations , 2019, Int. Trans. Oper. Res..

[2]  Thomas Walther,et al.  Oil price volatility forecast with mixture memory GARCH , 2016 .

[3]  S. P. Ghoshal,et al.  Dynamic electricity price forecasting using local linear wavelet neural network , 2015, Neural Computing and Applications.

[4]  George Halkos,et al.  Treating undesirable outputs in DEA: A critical review , 2019, Economic Analysis and Policy.

[5]  Ling Tang,et al.  A Novel CEEMD-Based EELM Ensemble Learning Paradigm for Crude Oil Price Forecasting , 2015, Int. J. Inf. Technol. Decis. Mak..

[6]  Joe Zhu,et al.  Super-efficiency infeasibility and zero data in DEA , 2012, Eur. J. Oper. Res..

[7]  Joe Zhu,et al.  A modified super-efficiency DEA model for infeasibility , 2009, J. Oper. Res. Soc..

[8]  International stock market contagion: A CEEMDAN wavelet analysis , 2018, Economic Modelling.

[9]  Yao Chen,et al.  Measuring super-efficiency in DEA in the presence of infeasibility , 2005, Eur. J. Oper. Res..

[10]  Zhongbao Zhou,et al.  Two-stage DEA models with undesirable input-intermediate-outputs $ , 2015 .

[11]  W. Poon,et al.  Do Oil Prices Predict Economic Growth? New Global Evidence , 2013 .

[12]  Fang Xu,et al.  A study of DEA models without explicit inputs , 2011 .

[13]  Seong-Min Yoon,et al.  Forecasting volatility of crude oil markets , 2009 .

[14]  Carolina García-Martos,et al.  Modelling and forecasting fossil fuels, CO2 and electricity prices and their volatilities , 2013 .

[15]  Bing Xu,et al.  A data envelopment analysis-based framework for the relative performance evaluation of competing crude oil prices' volatility forecasting models , 2012 .

[16]  Wenbin Liu,et al.  DEA Models via Goal Programming , 1999 .

[17]  P. Andersen,et al.  A procedure for ranking efficient units in data envelopment analysis , 1993 .

[18]  Ioannis E. Tsolas,et al.  Firm credit risk evaluation: a series two-stage DEA modeling framework , 2015, Ann. Oper. Res..

[19]  Zhongbao Zhou,et al.  Does international oil volatility have directional predictability for stock returns? Evidence from BRICS countries based on cross-quantilogram analysis , 2019, Economic Modelling.

[20]  Yaojie Zhang,et al.  Forecasting the oil futures price volatility: Large jumps and small jumps , 2018 .

[21]  Lawrence M. Seiford,et al.  INFEASIBILITY OF SUPER EFFICIENCY DATA ENVELOPMENT ANALYSIS MODELS , 1999 .

[22]  Georg Westermann,et al.  Data Envelopment Analysis in the Service Sector , 1999 .

[23]  Helu Xiao,et al.  DEA frontier improvement and portfolio rebalancing: An application of China mutual funds on considering sustainability information disclosure , 2017, Eur. J. Oper. Res..

[24]  Liang Liang,et al.  Super-efficiency DEA in the presence of infeasibility: One model approach , 2011, Eur. J. Oper. Res..

[25]  Helu Xiao,et al.  Estimation of portfolio efficiency via DEA , 2015 .

[26]  K. Tone,et al.  DEA IN PERFORMANCE EVALUATION OF CRUDE OIL PREDICTION MODELS , 2017 .