Relative Performance Evaluation of Competing Crude Oil Prices’ Volatility Forecasting Models: A Slacks-Based Super-Efficiency DEA Model

Abstract

With the increasing number of quantitative models available to forecast the volatility of crude oil prices, the assessment of the relative performance of competing models becomes a critical task. Our survey of the literature revealed that most studies tend to use several performance criteria to evaluate the performance of competing forecasting models; however, models are compared to each other using a single criterion at a time, which often leads to different rankings for different criteria-A situation where one cannot make an informed decision as to which model performs best when taking all criteria into account. In order to overcome this methodological problem, Xu and Ouenniche [1] proposed a multidimensional framework based on an input-oriented radial super-efficiency Data Envelopment Analysis (DEA) model to rank order competing forecasting models of crude oil prices' volatility. However, their approach suffers from a number of issues. In this paper, we overcome such issues by proposing an alternative framework.

Multi-criteria ranking of corporate distress prediction models: empirical evaluation and methodological contributions

“…To overcome this methodological drawback, Mousavi et al (2015) proposed a multi-criteria assessment framework; namely, an orientation-free super-efficiency data envelopment analysis. However, within a super-efficiency DEA framework, the reference benchmark changes from one efficient DMU evaluation to another, which in some contexts might be viewed as "unfair" benchmarking (Ouenniche et al, 2014). In this study, we overcome this issue by proposing a variant of the context-dependent DEA (CDEA) framework proposed by Seiford and Zhu (2003) which embed SBM models in the layering procedure.…”

DEA Scores’ Confidence Intervals with Past-Present and Past-Present-Future Based Resampling

“…In principle one might choose from a relatively wide range of DEA models; however, given the nature of this exercise we recommend the use of the non-oriented super slacks-based measure model (Tone [12] and Ouenniche et al [13]) under the relevant returns-to-scale (RTS) setup (e.g., constant, variable, increasing, decreasing) as suggested by the RTS analysis of the dataset one is dealing with. This model is an extension of the SBM (slacks-based measure) model of Tone [14]-see also [15].…”