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The Impact of Geopolitical Risk on Systemic Risk Spillover in Commodity Market: An EMD-Based Network Topology Approach

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  • Zhijing Ding
  • Xu Zhang
  • Baogui Xin

Abstract

Since the financialization of commodities, portfolio investments have become an important tool for investors to diversify risks. However, due to the nonlinear fluctuations brought about by extreme events, investors face more difficulties in the choice of risk portfolio. We adopt empirical mode decomposition and STVAR model, along with the basis data of optimized original sample interval. In addition, we retain the mature research of multiscale systemic risk under frequency and divide the dimension of systemic risk into two states. When frequency is combined with states, the risk spillover center undergoes subversive changes, particularly in the longest term, and metals become the risk spillover center, substituting the energy commodity, on the condition that the compositions of extreme value add persuasive power to the perspective of long term. We proposed that the joint fluctuation of agricultural commodities and energy commodities makes the former become another important risk spillover point. For investors, holding period and portfolio both need to be considered.

Suggested Citation

  • Zhijing Ding & Xu Zhang & Baogui Xin, 2021. "The Impact of Geopolitical Risk on Systemic Risk Spillover in Commodity Market: An EMD-Based Network Topology Approach," Complexity, Hindawi, vol. 2021, pages 1-17, July.
    • Handle: RePEc:hin:complx:2226944
    DOI: 10.1155/2021/2226944
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    Cited by:

    1. Faheem Aslam & Paulo Ferreira & Haider Ali & Ana Ercília José, 2022. "Application of Multifractal Analysis in Estimating the Reaction of Energy Markets to Geopolitical Acts and Threats," Sustainability, MDPI, vol. 14(10), pages 1-23, May.
    2. Yao, Qianyi & Fan, Ruguo & Chen, Rongkai & Qian, Rourou, 2023. "A model of the enterprise supply chain risk propagation based on partially mapping two-layer complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 613(C).
    3. Ouyang, Ruolan & Chen, Xiang & Fang, Yi & Zhao, Yang, 2022. "Systemic risk of commodity markets: A dynamic factor copula approach," International Review of Financial Analysis, Elsevier, vol. 82(C).
    4. Rangan Gupta & Sayar Karmakar & Christian Pierdzioch, 2022. "Safe Havens, Machine Learning, and the Sources of Geopolitical Risk: A Forecasting Analysis Using Over a Century of Data," Working Papers 202201, University of Pretoria, Department of Economics.
    5. Pacelli, Vincenzo & Miglietta, Federica & Foglia, Matteo, 2022. "The extreme risk connectedness of the new financial system: European evidence," International Review of Financial Analysis, Elsevier, vol. 84(C).
    6. Enilov, Martin & Mensi, Walid & Stankov, Petar, 2023. "Does safe haven exist? Tail risks of commodity markets during COVID-19 pandemic," Journal of Commodity Markets, Elsevier, vol. 29(C).
    7. Gong, Xu & Xu, Jun, 2022. "Geopolitical risk and dynamic connectedness between commodity markets," Energy Economics, Elsevier, vol. 110(C).

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