Multilayer network analysis of oil linkages

Abstract

Summary This manuscript proposes a new approach for unveiling existing linkages within the international oil market across multiple driving factors beyond production. A multilayer, multicountry network is extracted through a novel Bayesian graphical vector autoregressive model, which allows for a more comprehensive, dynamic representation of the network linkages than do traditional or static pairwise Granger-causal inference approaches. Building on previous work, 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 that unveil both static representations of world oil linkages and variations in microeconomic 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 the Arabian Peninsula and key Middle Eastern producers, with a country-based decomposition of production and rig deployment, while controlling for oil prices and global economic indices. Production is less affected by 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.

Factor Network Autoregressions

“…Several papers specifically investigate the characteristics of multilayer networks in economics and finance. Casarin et al (2020) use a Bayesian graphical VAR model to estimate adjacency matrices in an international multilayer network of oil market relationships. A number of other contributions focus on multilayer networks formed by financial institutions, where layers represent different types of financial instruments and contracts.…”

COVID-19 Spreading in Financial Networks: A Semiparametric Matrix Regression Model

“…The authors show that significant changes in connectivity on extreme risk and volatility spillover layers before a general financial turmoil. Casarin et al (2020) propose a Bayesian graphical vector autoregressive model to extract multilayer network in the international oil market and show that oil production network is a lagged driver for prices.…”

A Matrix-Variate t Model for Networks

“…The inferred network structure is intrinsically contaminated by a certain degree of estimation error, which may cumulate with other sources of errors, such as model misspecification and measurement error. Consequently, the direct use of estimated networks as inputs in network analyses (e.g., Casarin et al, 2020 ; Wang et al, 2021 ) may result in misleading conclusions. This calls for the definition of suitable tools for cleaning the data from random disturbances, thus enabling to perform valid statistical analyses of the networks.…”