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

Zhou, Zhongbao; Jin, Qianying; Peng, Jian; Xiao, Helu; Wu, Shijian

doi:10.3390/math7090827

Cited by 7 publications

(7 citation statements)

References 25 publications

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“…PEMs are thus widely applied by various researchers to evaluate and improve performance in a variety of industries and organizations. Such researchers include Chen and Chen [16], Hossain and Ahmed [20], Markovic' and Jankovic' [21], Wang, et al [17], Wong and Szeto [22], Basso and Funari [23], Wu et al [24], and Zhou et al [25]. In view of this, this study used PEMs to construct a model for the assessment, analysis, and provision of recommendations for e-learning systems, so as to improve the quality of these systems.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Evaluation Model for Enhancing E-Learning Systems

Lee

Wang

2019

Mathematics

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As the environment and information-technology conditions of the Internet of Things matured, various applications were launched. In education, e-learning is promoted so that students’ learning is no longer restricted to the classroom. E-learning schedules are flexible, and learners’ commuting costs are low. Apparently, improving the quality of e-learning systems can enhance learners’ learning effectiveness, satisfaction, engagement, and learning efficacy. A performance evaluation matrix is a useful tool for collecting users’ opinions to assess the performance of an operating system, and it is widely used to evaluate and improve performance in numerous industries and organizations. Therefore, this study used this matrix to construct a model for evaluation and analysis, providing suggestions on improving e-learning systems. This approach maintained the simple response model of Likert scales, which increases the efficiency and accuracy of data collection. Furthermore, the fuzzy membership function of the discriminant index was constructed based on the confidence interval, thereby solving the problems of sampling error and the complexity of collecting fuzzy linguistic data. Besides, we simplified calculations by standardizing test statistics to increase evaluation efficiency. As a result, this study improved the quality of e-learning system, enhanced users’ learning effectiveness, satisfaction, and engagement, and achieved the goal of sustainability.

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Section: Introductionmentioning

confidence: 99%

Fuzzy Evaluation Model for Enhancing E-Learning Systems

Lee

Wang

2019

Mathematics

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“…Prior studies probed into the relationship between oil prices and commodities. Zhou et al [45] reported an increasing number of investors who participate in the commodities markets, which strengthens the impact of oil price on commodity futures prices. Robe and Wallen [46] found a significant relationship between oil prices and the commodity futures market.…”

Section: Equity and Commodities Markets And Oilmentioning

confidence: 95%

“…Other studies also identified the return spillover from S&P 500 to WTI [42,44]. Moreover, Zhou et al [45] evaluated the co-movement between the volatility of the equity market proxied by S&P 500 and the oil market proxied by USO from 2007 to 2016. These authors found a difference between long-term and short-term data.…”

Section: Equity and Commodities Markets And Oilmentioning

confidence: 99%

Volatility Transmission from Equity, Bulk Shipping, and Commodity Markets to Oil ETF and Energy Fund—A GARCH-MIDAS Model

Lin

Chang

2020

Mathematics

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Oil continues to be a major source of world energy, but oil prices and funds have experienced high volatility over the last decade. This study applies the generalized autoregressive conditional heteroskedasticity-mixed-data sampling (GARCH-MIDAS) model on data spanning 1 July 2014 to 30 April 2020 to examine volatility transmission from the equity, bulk shipping, commodity, currency, and crude oil markets to the United States Oil Fund (USO) and BlackRock World Energy Fund A2 (BGF). By dividing the sample into two subsamples, we find a significant volatility transmission from the equity market to the oil ETF and energy fund both before and after the 2018 U.S.–China trade war. The volatility transmission from the bulk shipping, commodity, and crude oil markets turns significant for the oil ETF and energy fund after the 2018 U.S.–China trade war, extending into the COVID-19 pandemic in early 2020. The results suggest that investors can use the equity market to predict the movement of oil and energy funds during both tranquil and turmoil periods. Moreover, investors can use bulk shipping, commodity, and crude oil markets in addition to the equity market to forecast oil and energy funds’ volatility during the turmoil periods. This paper benefits investors against the high volatility of the energy funds.

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“…The volatility or the structural change is the common characteristic of the crude oil price. Crude oil prices are noisy, non stationary, non-linear, and unstructured in nature, which makes them very difficult to examine [13]. In order to evaluate the structural change of the time series, the Quandt-Andrews breakpoint test and the Bai-Perron test were used in this study to detect one or more unknown structural changes in the data.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Structural Changes in Financial Datasets Using the Breakpoint Test and the Markov Switching Model

Phoong

Phoong³

2020

Symmetry

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The price movements of commodities are determined by changes in the expectations about future economic variables. Crude oil price is non-stationary, highly volatile, and unstructured in nature, which makes it very difficult to predict over short-to-medium time horizons. Some analysts have indicated that the difficulty in forecasting the crude oil price is due to the fact that economic models cannot consistently show evidence of a strong connection between commodities and economic fundamentals, and, as a result, regarded the idea that economic fundamentals help predict price values as random luck. This study aimed to overcome the limitations of the economic models through the detection of structural changes as well as breaks in the data, using a breakpoint test. The Markov switching model is used to address the price patterns that led to a different market state. The results show that there are several changes as well as breaks in the estimated model. Moreover, there is an asymmetric correlation between the crude oil price and the GDP.

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

Cited by 7 publications

References 25 publications

Fuzzy Evaluation Model for Enhancing E-Learning Systems

Fuzzy Evaluation Model for Enhancing E-Learning Systems

Volatility Transmission from Equity, Bulk Shipping, and Commodity Markets to Oil ETF and Energy Fund—A GARCH-MIDAS Model

Analysis of Structural Changes in Financial Datasets Using the Breakpoint Test and the Markov Switching Model

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