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A conceptual framework for the oil market dynamics: A systems approach
Author(s) -
Seyed Hossein Hosseini,
Hamed Shakouri G.,
Adel Peighami
Publication year - 2016
Publication title -
energy exploration and exploitation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.435
H-Index - 30
eISSN - 2048-4054
pISSN - 0144-5987
DOI - 10.1177/0144598716631656
Subject(s) - causal loop diagram , system dynamics , futures contract , conceptual framework , granger causality , economics , systems thinking , econometrics , univariate , computer science , operations research , financial economics , engineering , multivariate statistics , philosophy , epistemology , artificial intelligence , machine learning
The recent decade witnessed both steep ascending and descending trends in global oil prices. As a main energy resource, oil has been playing a substantial role in contemporary world’s economics. Hence, analyzing and understanding short- to long-term dynamics of oil prices is still one of challenging issues in the global economic- and energy-related debate topics. Although the literature is full of significant analytical studies that focus on particular issues related to the real oil prices, less can be found that integrates various factors affecting its dynamics. An integrated model, in which focal variables are included in main building blocks are illustrated with their interrelationships, helps policy makers to better understand the system. In this article, using a systems approach, a conceptual framework is developed to demonstrate various (economic and financial, technological, political, demographic, and industrial) factors that impact on the dynamics of the futures and spot prices with their interrelations. It is shown that unilateral (and univariate) analyzes is not sufficient in oil market dynamics analysis and systems approach should be applied. To do so, a subsystems diagram is developed based on literature review and analysis of oil market statistics. To validate the framework, windowed correlation analysis, Granger causality test, and regression analysis are utilized. Accordingly, a causal loop diagram is developed to describe interrelationships between main variables making the dynamics of oil prices. The framework provides practitioners with a foundation to conduct different related analyses. A quantified system dynamics model can be built based on this framework to simulate the market behavior and predict probable future trends and fluctuations.

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