A Micro-Based Model for World Oil Market

Multilayer network analysis of oil linkages

“…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).…”

“…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.…”

“…The approach proposed in this paper builds on Rousan et al (2018) (multi-country, single-layer) and Espinasa et al (2017) (multi-layer, single country), yet it differs from those two constrained perspectives of the linkage exploration problem by providing a dynamic,…”

“…Electronic copy available at: https://ssrn.com/abstract=3263535 yet complete network representation of the linkages across (and while controlling for) a wider set of relevant factors identified at the micro level. This study also contributes to the recent and expanding stream of literature on applied network econometrics (Diebold and Yilmaz, 2014), by adapting Bayesian graphical model for SVAR processes Ahelegbey et al (2016a) to the expanded oil network structure studied in (Espinasa et al, 2017), leading to jointly estimating the shape of the world oil network. Also the findings in this paper are not feasible with the aforementioned approaches, as some dynamic linkages occur at the inter-factor level.…”

“…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.…”