IDEAS home Printed from https://ideas.repec.org/a/gam/jijfss/v13y2025i2p97-d1670155.html

Dynamic Spillover Effects Among China’s Energy, Real Estate, and Stock Markets: Evidence from Extreme Events

Author

Listed:
  • Fusheng Xie

    (Department of Fintech, Shanghai Normal University Tianhua College, Shengxin North Road Campus, Shanghai 201815, China)

  • Jingbo Wang

    (Business Analysis, The University of Sydney Business School, Darlington Campus, Sydney 2006, Australia)

  • Chunzi Wang

    (Department of Fintech, Shanghai Normal University Tianhua College, Shengxin North Road Campus, Shanghai 201815, China)

Abstract

This paper employs a Time-Varying Parameter Vector Autoregression Directional–Spillover (TVP-VAR-DY) model to investigate the dynamic spillover effects among China’s energy, real estate, and stock markets from 2013 to 2023, with a focus on the impact of extreme events. The findings show that the total conditional spillover index (TCI) typically remains below 40% in the absence of extreme events, but significantly increases during such events, reaching 51.09% during the 2015 stock market crisis and nearing 60% during the COVID-19 pandemic in 2020. Specifically, the oil and gas market exhibited a net spillover index of 4.61%, emerging as a major source of risk transmission. In contrast, the real estate market, which had a net spillover index of −9.38%, became a net risk absorber. The net spillover index indicates that the risk transmission role of different markets towards other markets is dynamically changing over time and is closely related to significant global or domestic economic events. These results indicate that extreme events not only directly impact specific markets but also rapidly propagate risks through complex inter-market linkages, exacerbating systemic risks. Therefore, it is recommended to enhance market monitoring, improve transparency, and optimize risk management strategies to cope with uncertainties in the global economy and financial markets.

Suggested Citation

  • Fusheng Xie & Jingbo Wang & Chunzi Wang, 2025. "Dynamic Spillover Effects Among China’s Energy, Real Estate, and Stock Markets: Evidence from Extreme Events," IJFS, MDPI, vol. 13(2), pages 1-21, June.
    • Handle: RePEc:gam:jijfss:v:13:y:2025:i:2:p:97-:d:1670155
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7072/13/2/97/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7072/13/2/97/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Koop, Gary & Korobilis, Dimitris, 2013. "Large time-varying parameter VARs," Journal of Econometrics, Elsevier, vol. 177(2), pages 185-198.
    2. Mensi, Walid & Al Rababa'a, Abdel Razzaq & Vo, Xuan Vinh & Kang, Sang Hoon, 2021. "Asymmetric spillover and network connectedness between crude oil, gold, and Chinese sector stock markets," Energy Economics, Elsevier, vol. 98(C).
    3. Akbari, Amir & Krystyniak, Karolina, 2021. "Government real estate interventions and the stock market," International Review of Financial Analysis, Elsevier, vol. 75(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. Zhang, Yaojie & Tian, Linxing & Zhang, Zhikai, 2025. "Petroleum volatility spillover index and stock return predictability," Energy Economics, Elsevier, vol. 150(C).
    2. Zynobia Barson & Kwame Simpe Ofori & Peterson Owusu Junior & Kwabena G. Boakye & George Oppong Appiagyei Ampong, 2024. "Time-varying Connectedness Between ESG Stocks and BRVM Traditional Stocks," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 23(3), pages 306-335, September.
    3. Chen, Chuanglian & Zhou, Lichao & Sun, Chuanwang & Lin, Yuting, 2024. "Does oil future increase the network systemic risk of financial institutions in China?," Applied Energy, Elsevier, vol. 364(C).
    4. Ghaemi Asl, Mahdi & Adekoya, Oluwasegun Babatunde & Rashidi, Muhammad Mahdi & Ghasemi Doudkanlou, Mohammad & Dolatabadi, Ali, 2022. "Forecast of Bayesian-based dynamic connectedness between oil market and Islamic stock indices of Islamic oil-exporting countries: Application of the cascade-forward backpropagation network," Resources Policy, Elsevier, vol. 77(C).
    5. repec:rim:rimwps:18-20 is not listed on IDEAS
    6. Hongjun Zeng & Abdullahi D. Ahmed, 2026. "Dependency structure and volatility connectedness among China-ASEAN stock market, cryptocurrencies, and crude oil," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 12(1), pages 1-39, December.
    7. Le, Trung H. & Pham, Linh & Do, Hung X., 2023. "Price risk transmissions in the water-energy-food nexus: Impacts of climate risks and portfolio implications," Energy Economics, Elsevier, vol. 124(C).
    8. Catania, Leopoldo & Grassi, Stefano & Ravazzolo, Francesco, 2019. "Forecasting cryptocurrencies under model and parameter instability," International Journal of Forecasting, Elsevier, vol. 35(2), pages 485-501.
    9. Joseph P. Byrne & Dimitris Korobilis & Pinho J. Ribeiro, 2018. "On The Sources Of Uncertainty In Exchange Rate Predictability," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 59(1), pages 329-357, February.
    10. Chen, Bin-xia & Sun, Yan-lin, 2024. "Risk characteristics and connectedness in cryptocurrency markets: New evidence from a non-linear framework," The North American Journal of Economics and Finance, Elsevier, vol. 69(PA).
    11. Yousfi, Mohamed & Farhani, Ramzi & Bouzgarrou, Houssam, 2024. "From the pandemic to the Russia–Ukraine crisis: Dynamic behavior of connectedness between financial markets and implications for portfolio management," Economic Analysis and Policy, Elsevier, vol. 81(C), pages 1178-1197.
    12. Lyu, Chenyan & Do, Hung Xuan & Nepal, Rabindra & Jamasb, Tooraj, 2024. "Volatility spillovers and carbon price in the Nordic wholesale electricity markets," Energy Economics, Elsevier, vol. 134(C).
    13. Samuel F. Onipede & Nafiu A. Bashir & Jamaladeen Abubakar, 2023. "Small open economies and external shocks: an application of Bayesian global vector autoregression model," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(2), pages 1673-1699, April.
    14. Hauzenberger, Niko, 2021. "Flexible Mixture Priors for Large Time-varying Parameter Models," Econometrics and Statistics, Elsevier, vol. 20(C), pages 87-108.
    15. Joshua C. C. Chan, 2018. "Specification tests for time-varying parameter models with stochastic volatility," Econometric Reviews, Taylor & Francis Journals, vol. 37(8), pages 807-823, September.
    16. Chen, An-Sing & Nguyen, Huong Thi, 2024. "A new perspective on how investor sentiment affects herding behavior in the cryptocurrency market," Finance Research Letters, Elsevier, vol. 67(PA).
    17. Drachal, Krzysztof, 2021. "Forecasting crude oil real prices with averaging time-varying VAR models," Resources Policy, Elsevier, vol. 74(C).
    18. Huang, Jionghao & Chen, Baifan & Xu, Yushi & Xia, Xiaohua, 2023. "Time-frequency volatility transmission among energy commodities and financial markets during the COVID-19 pandemic: A Novel TVP-VAR frequency connectedness approach," Finance Research Letters, Elsevier, vol. 53(C).
    19. Chatziantoniou, Ioannis & Gabauer, David & Stenfors, Alexis, 2020. "From CIP-deviations to a market for risk premia: A dynamic investigation of cross-currency basis swaps," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 69(C).
    20. Chen, Yiguo & Luo, Peng & Chang, Tsangyao, 2025. "Economic Nexus among the Belt and Road Initiative participating countries," The North American Journal of Economics and Finance, Elsevier, vol. 77(C).
    21. Chen, Liangyuan & Lin, Jianhao & Wang, Xia & Zhu, Zixiang, 2025. "The real effect of China's narrative Central Bank Communication," China Economic Review, Elsevier, vol. 94(PC).

    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:jijfss:v:13:y:2025:i:2:p:97-:d:1670155. 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.