Multilayer Network Analysis of Oil Linkages

32 Pages Posted: 1 Nov 2018

See all articles by Roberto Casarin

Roberto Casarin

University Ca' Foscari of Venice - Department of Economics

Enrique ter Horst

Universidad de los Andes, Colombia - School of Business Administration

German Molina

Idalion Capital US LP

Ramon Espinasa

Inter-American Development Bank (IDB)

Carlos Sucre

Independent

Roberto Rigobon

Massachusetts Institute of Technology (MIT) - Sloan School of Management; National Bureau of Economic Research (NBER)

Date Written: October 9, 2018

Abstract

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 inter- connected 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.

Keywords: Bayesian Graphical Models, Dynamic Multilayer Network analysis, Rigs, Pro- duction, Granger Causality, Oil Linkages

JEL Classification: C11

Suggested Citation

Casarin, Roberto and ter Horst, Enrique and Molina, German and Espinasa, Ramon and Sucre, Carlos and Rigobon, Roberto, Multilayer Network Analysis of Oil Linkages (October 9, 2018). Available at SSRN: https://ssrn.com/abstract=3263535 or http://dx.doi.org/10.2139/ssrn.3263535

Roberto Casarin

University Ca' Foscari of Venice - Department of Economics ( email )

San Giobbe 873/b
Venice, 30121
Italy
+39 030.298.91.49 (Phone)
+39 030.298.88.37 (Fax)

HOME PAGE: http://sites.google.com/view/robertocasarin

Enrique ter Horst (Contact Author)

Universidad de los Andes, Colombia - School of Business Administration ( email )

Bogota
Colombia

German Molina

Idalion Capital US LP ( email )

2711 Centerville Rd Ste 400
Wilmington, DE 19808
United States

Ramon Espinasa

Inter-American Development Bank (IDB) ( email )

1300 New York Avenue NW
Washington, DC 20577
United States

Carlos Sucre

Independent

Roberto Rigobon

Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )

E52-447
Cambridge, MA 02142
United States
617-258-8374 (Phone)
617-258-6855 (Fax)

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

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