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.
Highlights
Among energy sources, crude oil remains the major part of energy consumption
The current study finds that with undesirable inputs, infeasibility in the input-oriented model (Model 1) indicates super-efficiency can be regarded as output surplus while infeasibility in the output-oriented model (Model 2) indicates super-efficiency can be regarded as undesirable input surplus
The desirability of inputs and outputs are of great significance to its application. This contribution focuses on these two aspects and proposes a modified super efficiency data envelopment analysis (DEA) framework for evaluating forecasting models
Summary
Crude oil remains the major part of energy consumption. Its price volatility is closely related to the stability of both the macro-economy and micro-economy [1]. The importance of crude oil prices in industry attracts great attention among academies to forecast the volatilities of crude oil prices. Statistical forecasting models represent one type of the main crude oil price volatility forecasting models, refer to [2,3], etc. Based on the classic Generalized Autoregressive Conditional. Integrated GARCH (FIGARCH) models are useful for modelling and forecasting persistence in the volatility of crude oil prices [4]. Tang et al (2015) proposed a novel complementary ensemble empirical mode decomposition (CEEMD) based extended extreme learning machine (EELM)
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