摘要
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.
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.
