Visibility graph analysis of crude oil futures markets: Insights from the COVID-19 pandemic and Russia-Ukraine conflict

ArXiv ID: 2310.18903 “View on arXiv”

Authors: Unknown

Abstract

Drawing inspiration from the significant impact of the ongoing Russia-Ukraine conflict and the recent COVID-19 pandemic on global financial markets, this study conducts a thorough analysis of three key crude oil futures markets: WTI, Brent, and Shanghai (SC). Employing the visibility graph (VG) methodology, we examine both static and dynamic characteristics using daily and high-frequency data. We identified a clear power-law decay in most VG degree distributions and highlighted the pronounced clustering tendencies within crude oil futures VGs. Our results also confirm an inverse correlation between clustering coefficient and node degree and further reveal that all VGs not only adhere to the small-world property but also exhibit intricate assortative mixing. Through the time-varying characteristics of VGs, we found that WTI and Brent demonstrate aligned behavior, while the SC market, with its unique trading mechanics, deviates. The 5-minute VGs’ assortativity coefficient provides a deeper understanding of these markets’ reactions to the pandemic and geopolitical events. Furthermore, the differential responses during the COVID-19 and Russia-Ukraine conflict underline the unique sensitivities of each market to global disruptions. Overall, this research offers profound insights into the structure, dynamics, and adaptability of these essential commodities markets in the face of worldwide challenges.

Keywords: Visibility Graph (VG), Power-Law Decay, Clustering Coefficient, Small-World Property, Assortative Mixing, Commodities (Crude Oil Futures)

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail
  • Why: The paper employs advanced network theory concepts like visibility graphs, power-law distributions, and small-world properties, requiring dense mathematical formalism. It also demonstrates high empirical rigor by analyzing real high-frequency and daily data from multiple crude oil futures markets (WTI, Brent, Shanghai) across specific crisis periods (COVID-19, Russia-Ukraine conflict) and reporting detailed statistical metrics (degree distributions, clustering coefficients, assortativity).
  flowchart TD
    A["Research Goal<br>Analyze crude oil futures market dynamics<br>under COVID-19 & Russia-Ukraine conflict"] --> B["Methodology<br>Visibility Graph (VG) Analysis"]
    B --> C{"Data Inputs<br>Daily & 5-minute futures prices<br>WTI, Brent, Shanghai SC"}
    C --> D["Computational Process<br>Construct Static & Dynamic VGs"]
    D --> E{"Analyze Network Properties<br>Degree Distribution<br>Clustering Coefficient<br>Assortativity"}
    E --> F["Key Findings<br>Power-law decay & Small-World property<br>WTI & Brent aligned; SC deviates<br>Unique sensitivities to global shocks"]