A micro-based model for world oil market

Espinasa, Ramón; Horst, Enrique ter; Reyes, Sergio Guerra; Manzano, Osmel; Molina, Germán; Rigobón, Roberto

doi:10.1016/j.eneco.2017.06.019

Cited by 15 publications

(21 citation statements)

References 12 publications

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“…Until 1990, demand shocks have decreased production. Espinasa [73] highlights Saudi Arabia's sensitivity to prices, especially in the first six months. Drachal [74] suggests the important deflation role of inventories even in 1991.…”

Section: Pwy (2011)mentioning

confidence: 99%

Detecting West Texas Intermediate (WTI) Prices’ Bubble Periods

Perifanis¹

2019

Energies

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Oil prices have had considerable surges and bursts since the first oil crisis of 1973. Until then its price was stable, with almost zero volatility. Since then, apart from the two oil crises of 1973 and 1978/9, oil prices had consecutive bubble episodes like the surges up to 2008 and 2014 and their successive bursts, respectively. The trace of these bubble periods is of crucial importance for policymakers, since their drivers and consequences impact global economic developments. Phillips et al. and Phillips et al. methodologies are applied to detect whether West Texas Intermediate prices experienced bubble periods. Both methodologies suggest that WTI prices experienced explosive episodes, which could be fundamentally, speculatively, or politically attributed. Some suggested periods coincide for both methods, but the second methodology seems to be more sensitive than its predecessor is, leading to better bubble detection but also to identification of non-existent bubbles. The identified bubble periods are compared to relevant research in the literature concerning their presence, duration, and explosiveness. The main goal of the research, apart from the detection of bubbles’ presence and duration, is to identify the causal underlying reasons for each explosive episode. Further, we compare the start and endpoints of each bubble episode with time-points when structural changes occurred. The contribution of the paper is that it clearly defines the bubble episodes with their corresponding drivers. The paper identifies the importance of market fundamentals’ swifts in explaining the bubble periods. The findings of the papers can help policymakers and other stakeholders to monitor oil price shifts and their underlying reasons, and then proceed with prompt actions. Since bubble episodes are fundamentally explained, then the practical utility is that by focusing on the market fundamentals, stakeholders can avoid actions that could result in market failures.

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Section: Pwy (2011)mentioning

confidence: 99%

Detecting West Texas Intermediate (WTI) Prices’ Bubble Periods

Perifanis¹

2019

Energies

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“…This type of causal relationship can be naturally modelled through structural vector autoregressive (SVAR) time series models. The SVAR approach is commonly used in the econometric literature [49], and the resulting reduced form model allows for causal interpretability of the parameters of interest, rather than assessment of co-relationships.…”

Section: Methodsmentioning

confidence: 99%

Online interest regarding violent attacks, gun control, and gun purchase: A causal analysis

et al. 2018

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BackgroundIncreased interest about gun ownership and gun control are oftentimes driven by informational shocks in a common factor, namely violent attacks, and the perceived need for higher levels of safety. A causal depiction of the societal interest around violent attacks, gun control and gun purchase, both synchronous and over time, should be a stepping stone for designing future strategies regarding the safety concerns of the U.S. population.ObjectiveExamine the causal relationships between unexpected increases in population interest about violent attacks, gun control, and gun purchase.MethodsRelationships among online searches for information about violent attacks, gun control, and gun purchase occurring between 2004 and 2017 in the U.S. are explained through a novel structural vector autoregressive time series model to account for simultaneous causal relationships.ResultsMore than 20% of the stationary variability in each of gun control and gun purchase interest can be explained by the remaining factors. Gun control interest appears to be caused, in part, by violent attacks informational shocks, yet violent attacks, although impactful, have a lesser effect than gun control debate on long-term gun ownership interests.ConclusionsThe form in which gun control has been introduced in public debate may have further increased gun ownership interest. Reactive gun purchase interest may be an unintended side effect of gun control debate. U.S. policymakers may need to rethink current approaches to promotion of gun control, and whether societal policy debate without policy outcomes could be having unintended effects.

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“…Oil markets provide a natural application of network models due to their highly interconnected nature (Economou et al, 2017). Multiple approaches have been proposed to the use of network models in oil markets, either isolating the production-only relationships and providing a dynamic representation of production-driven linkages (Rousan et al, 2018), or providing a fuller picture of the micro-level relationships, but offering only a static approach to their linkages (Espinasa et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…As discussed in Espinasa et al (2017), production of oil consists of several steps: exploration, drilling, extraction, and commercialization. When demand for oil increases and prices rise, the supply chain responds by increasing exploration efforts and drilling activity, which is followed by increased production levels.…”

Section: Introductionmentioning

confidence: 99%

“…A multi-layer, multi-country network is extracted through a novel Bayesian graphical vector autoregressive model, which allows for a more comprehensive, dynamic representation of the network linkages than traditional or static pairwise Granger-causal inference approaches. Building on the complementary strengths in Espinasa et al (2017) and Rousan et al (2018), the layers of the network include country-and region-specific oil production levels and rigs, both through simultaneous and lagged temporal dependences among key factors, while controlling for oil prices and a world economic activity index. The proposed approach extracts relationships across all variables through a dynamic, cross-regional network.…”

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confidence: 99%

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Multilayer Network Analysis of Oil Linkages

et al. 2018

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This manuscript proposes a new approach for unveiling existing linkages within the international oil market across multiple driving factors beyond production. A multi-layer, multi-country network is extracted through a novel Bayesian graphical vector autoregressive model, which allows for a more comprehensive, dynamic representation of the network linkages than traditional or static pairwise Granger-causal inference approaches. Building on the complementary strengths in Espinasa et al. (2017) and Rousan et al. (2018), the layers of the network include country-and region-specific oil production levels and rigs, both through simultaneous and lagged temporal dependences among key factors, while controlling for oil prices and a world economic activity index. The proposed approach extracts relationships across all variables through a dynamic, cross-regional network. This approach is highly scalable, and adjusts for time-evolving linkages. The model outcome is a set of time-varying graphical networks which unveil both static representations of world oil linkages and variations in micro-economic relationships both within and between oil producers. An example is provided, illustrating the evolution of intra-and inter-regional relationships for two major interconnected oil producers: the United States, with a regional decomposition of its production and rig deployment, and Arabian Peninsula and key middle east producers, with a country-based decomposition of production and rig deployment, while controlling for oil prices and global economic indices. Production is less affected to concurrent changes in oil prices and the overall economy than rigs. However, production is a lagged driver for prices, rather than rigs, which indicates that the linkage between rigs and production may not be fully accounted for in the markets.

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A micro-based model for world oil market

Cited by 15 publications

References 12 publications

Detecting West Texas Intermediate (WTI) Prices’ Bubble Periods

Detecting West Texas Intermediate (WTI) Prices’ Bubble Periods

Online interest regarding violent attacks, gun control, and gun purchase: A causal analysis

Multilayer Network Analysis of Oil Linkages

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