IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i23p5852-d1526904.html
   My bibliography  Save this article

Dynamic Anomaly Detection in the Chinese Energy Market During Financial Turbulence Using Ratio Mutual Information and Crude Oil Price Movements

Author

Listed:
  • Lin Xiao

    (Faculty of Economics and Business Administration, Yibin Unversity, Yibin 644000, China
    Key Laboratory of Urban-Rural Industrial Integration and Intelligent Decision-Making, Sichuan Province for Social Sciences, Yibin 644000, China)

  • Arash Sioofy Khoojine

    (Faculty of Economics and Business Administration, Yibin Unversity, Yibin 644000, China)

Abstract

Investigating the stability of and fluctuations in the energy market has long been of interest to researchers and financial market participants. This study aimed to analyze the Chinese energy market, focusing on its volatility and response to financial tensions. For this purpose, data from eight major financial companies, which were selected based on their market share in Shanghai’s and Shenzhen’s financial markets, were collected from January 2014 to December 2023. In this study, stock prices and trading volumes were used as the key variables to build bootstrap-based minimum spanning trees (BMSTs) using ratio mutual information (RMI). Then, using the sliding window procedure, the major network characteristics were derived to create an anomaly-detection tool using the multivariate exponentially weighted moving average (MEWMA), along with the Brent crude oil price index as a benchmark and a global oil price indicator. This framework’s stability was evaluated through stress testing with five scenarios designed for this purpose. The results demonstrate that during periods of high oil price volatility, such as during the turbulence in the stock market in 2015 and the COVID-19 pandemic in 2020, the network topologies became more centralized, which shows that the market’s instability increased. This framework successfully identifies anomalies and proves to be a valuable tool for market players and policymakers in evaluating companies that are active in the energy sector and predicting possible instabilities, which could be useful in monitoring financial markets and improving decision-making processes in the energy sector. In addition, the integration of other macroeconomic factors into this field could strengthen the identification of anomalies and be considered a field for possible research.

Suggested Citation

  • Lin Xiao & Arash Sioofy Khoojine, 2024. "Dynamic Anomaly Detection in the Chinese Energy Market During Financial Turbulence Using Ratio Mutual Information and Crude Oil Price Movements," Energies, MDPI, vol. 17(23), pages 1-22, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:23:p:5852-:d:1526904
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/23/5852/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/23/5852/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ahmed, Abdullahi D. & Huo, Rui, 2021. "Volatility transmissions across international oil market, commodity futures and stock markets: Empirical evidence from China," Energy Economics, Elsevier, vol. 93(C).
    2. Arash Sioofy Khoojine & Ziyun Feng & Mahboubeh Shadabfar & Negar Sioofy Khoojine, 2023. "Analyzing volatility patterns in the Chinese stock market using partial mutual information-based distances," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 96(12), pages 1-21, December.
    3. Restrepo, Natalia & Uribe, Jorge M. & Manotas, Diego, 2018. "Financial risk network architecture of energy firms," Applied Energy, Elsevier, vol. 215(C), pages 630-642.
    4. Xi, Xian & An, Haizhong, 2018. "Research on energy stock market associated network structure based on financial indicators," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1309-1323.
    5. Joel Bun & Romain Allez & Jean-Philippe Bouchaud & Marc Potters, 2015. "Rotational invariant estimator for general noisy matrices," Papers 1502.06736, arXiv.org, revised Oct 2016.
    6. Borysov, Petro & Hannig, Jan & Marron, J.S., 2014. "Asymptotics of hierarchical clustering for growing dimension," Journal of Multivariate Analysis, Elsevier, vol. 124(C), pages 465-479.
    7. Li, Jie & Li, Ping, 2021. "Empirical analysis of the dynamic dependence between WTI oil and Chinese energy stocks," Energy Economics, Elsevier, vol. 93(C).
    8. Omid Farkhondeh Rouz & Hossein Sohrabi Vafa & Arash Sioofy Khoojine & Sajjad Pashay Amiri, 2024. "Interconnectedness of systemic risk in the Chinese economy: the Granger causality and CISS indicator approach," Risk Management, Palgrave Macmillan, vol. 26(2), pages 1-24, May.
    9. Gu, Qinen & Li, Shaofang & Tian, Sihua & Wang, Yuyouting, 2024. "Impact of climate risk on energy market risk spillover: Evidence from dynamic heterogeneous network analysis," Energy Economics, Elsevier, vol. 137(C).
    10. Sama Haddad, 2023. "Global Financial Market Integration: A Literature Survey," JRFM, MDPI, vol. 16(12), pages 1-27, November.
    11. Arash Sioofy Khoojine & Dong Han, 2020. "Stock price network autoregressive model with application to stock market turbulence," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 93(7), pages 1-15, July.
    12. Alexander P. Kartun-Giles & Nadia Ameli, 2023. "An Introduction to Complex Networks in Climate Finance," Papers 2309.15890, arXiv.org.
    13. Edward M. H. Lin & Edward W. Sun & Min-Teh Yu, 2018. "Systemic risk, financial markets, and performance of financial institutions," Annals of Operations Research, Springer, vol. 262(2), pages 579-603, March.
    14. Richmond, Peter & Mimkes, Jurgen & Hutzler, Stefan, 2013. "Econophysics and Physical Economics," OUP Catalogue, Oxford University Press, number 9780199674701.
    15. Li, Zhezhou & Chen, Shengchen & Chang, Xiyang, 2024. "Achieving clean energy via economic stability to qualify sustainable development goals in China," Economic Analysis and Policy, Elsevier, vol. 81(C), pages 1382-1394.
    16. Sun, Bowen & Li, Huajiao & An, Pengli & Wang, Ze, 2020. "Dynamic energy stock selection based on shareholders’ coholding network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    17. Gong, Xiao-Li & Feng, Yong-Kang & Liu, Jian-Min & Xiong, Xiong, 2023. "Study on international energy market and geopolitical risk contagion based on complex network," Resources Policy, Elsevier, vol. 82(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dong, Zhiliang & An, Haizhong & Liu, Sen & Li, Zhengyang & Yuan, Meng, 2020. "Research on the time-varying network structure evolution of the stock indices of the BRICS countries based on fluctuation correlation," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 63-74.
    2. Liu, Jiahao & Zhu, Bo & Hu, Xin, 2024. "Systemic risk spillovers among global energy firms: Does geopolitical risk matter?," Energy Economics, Elsevier, vol. 140(C).
    3. Corbet, Shaen & Hou, Yang (Greg) & Hu, Yang & Oxley, Les, 2022. "The growth of oil futures in China: Evidence of market maturity through global crises," Energy Economics, Elsevier, vol. 114(C).
    4. Gautier Marti & Frank Nielsen & Miko{l}aj Bi'nkowski & Philippe Donnat, 2017. "A review of two decades of correlations, hierarchies, networks and clustering in financial markets," Papers 1703.00485, arXiv.org, revised Nov 2020.
    5. Tao, Miaomiao & Roubaud, David & Tiwari, Aviral Kumar & Silva, Emilson, 2025. "How do systematic risk spillovers reshape investment outcomes?," Finance Research Letters, Elsevier, vol. 75(C).
    6. Chen, Tingqiang & Zheng, Xin & Wang, Lei, 2025. "Systemic risk among Chinese oil and petrochemical firms based on dynamic tail risk spillover networks," The North American Journal of Economics and Finance, Elsevier, vol. 77(C).
    7. Gautier Marti & Philippe Very & Philippe Donnat & Frank Nielsen, 2015. "A proposal of a methodological framework with experimental guidelines to investigate clustering stability on financial time series," Papers 1509.05475, arXiv.org.
    8. Mensi, Walid & Rehman, Mobeen Ur & Vo, Xuan Vinh, 2022. "Spillovers and diversification benefits between oil futures and ASEAN stock markets," Resources Policy, Elsevier, vol. 79(C).
    9. Egashira, Kento & Yata, Kazuyoshi & Aoshima, Makoto, 2024. "Asymptotic properties of hierarchical clustering in high-dimensional settings," Journal of Multivariate Analysis, Elsevier, vol. 199(C).
    10. Hutzler, S. & Sommer, C. & Richmond, P., 2016. "On the relationship between income, fertility rates and the state of democracy in society," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 9-18.
    11. Venkatasubramanian, Venkat & Luo, Yu & Sethuraman, Jay, 2015. "How much inequality in income is fair? A microeconomic game theoretic perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 435(C), pages 120-138.
    12. Wu, Fei & Zhang, Dayong & Ji, Qiang, 2021. "Systemic risk and financial contagion across top global energy companies," Energy Economics, Elsevier, vol. 97(C).
    13. Xie, Qichang & Luo, Chao & Cong, Xiaoping & Wang, Xu, 2024. "Volatility connectedness and its determinants of global energy stock markets," Economic Systems, Elsevier, vol. 48(2).
    14. Sun, Bowen & Li, Huajiao & An, Pengli & Wang, Ze, 2020. "Dynamic energy stock selection based on shareholders’ coholding network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    15. Roberto Cellini & Tiziana Cuccia & Johan Lyrvall, 2023. "On the price dynamics of non-fungible tokens: The ‘Bored Apes’ case," PLOS ONE, Public Library of Science, vol. 18(11), pages 1-20, November.
    16. Mercure, Jean-François, 2018. "Fashion, fads and the popularity of choices: Micro-foundations for diffusion consumer theory," Structural Change and Economic Dynamics, Elsevier, vol. 46(C), pages 194-207.
    17. Richard Audoly & Rory McGee & Sergio Ocampo & Gonzalo Paz-Pardo, 2024. "The Life-Cycle Dynamics of Wealth Mobility," Staff Reports 1097, Federal Reserve Bank of New York.
    18. Giang Thi Huong Vuong & Manh Huu Nguyen & Khanh Hoang, 2024. "Oil price uncertainty, oil pricing reform, and corporate profitability: The case of China," PLOS ONE, Public Library of Science, vol. 19(2), pages 1-25, February.
    19. Emmanuelle Jay & Thibault Soler & Eugénie Terreaux & Jean-Philippe Ovarlez & Frédéric Pascal & Philippe de Peretti & Christophe Chorro, 2020. "Improving portfolios global performance using a cleaned and robust covariance matrix estimate," Post-Print hal-02508748, HAL.
    20. Fenghua Wen & Yujie Yuan & Wei‐Xing Zhou, 2021. "Cross‐shareholding networks and stock price synchronicity: Evidence from China," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 914-948, January.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:23:p:5852-:d:1526904. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.