TITLE:
Relative Performance Evaluation of Competing Crude Oil Prices’ Volatility Forecasting Models: A Slacks-Based Super-Efficiency DEA Model
AUTHORS:
Jamal Ouenniche, Bing Xu, Kaoru Tone
KEYWORDS:
Forecasting Crude Oil Prices’ Volatility, Performance Evaluation, Slacks-Based Measure (SBM), Data Envelopment Analysis (DEA), Commodity and Energy Markets
JOURNAL NAME:
American Journal of Operations Research,
Vol.4 No.4,
July
10,
2014
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.