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Interconnected networks: Measuring extreme risk connectedness between China’s financial sector and real estate sector

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  • Ouyang, Zisheng
  • Zhou, Xuewei

Abstract

We propose interconnected networks through the network VAR model, which can simultaneously investigate the risk connectedness of intra- and inter-layer. Using the conditional autoregressive value at risk (CAViaR) tool to measure the extreme risk of financial firms and real estate firms, we apply the proposed approach to examine the extreme risk connectedness between China’s financial sector and real estate sector. We also identify the key drivers of extreme risk spillovers between sectors by constructing the inter-sector systemic importance indicator. Our results show that isolated networks underestimate extreme risk connectedness in the financial and real estate sectors, and there are significant differences between isolated networks and interconnected networks. There are lots of extreme risk spillovers between the financial sector and the real estate sector, where the extreme risk spillover from the real estate sector to the financial sector is stronger than the reverse extreme risk spillover. According to firm-level analysis, we observe that financial firms are the main risk receivers in interconnected networks, while real estate firms are the main risk transmitters. Also, we notice that financial firms are key nodes connecting inter-sector risk spillovers. Our conclusions provide new insights into preventing and defusing systemic risks.

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  • Ouyang, Zisheng & Zhou, Xuewei, 2023. "Interconnected networks: Measuring extreme risk connectedness between China’s financial sector and real estate sector," International Review of Financial Analysis, Elsevier, vol. 90(C).
    • Handle: RePEc:eee:finana:v:90:y:2023:i:c:s1057521923004088
    DOI: 10.1016/j.irfa.2023.102892
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    Cited by:

    1. Ouyang, Zisheng & Zhou, Xuewei & Wang, Gang-jin & Liu, Shuwen & Lu, Min, 2024. "Multilayer networks in the frequency domain: Measuring volatility connectedness among Chinese financial institutions," International Review of Economics & Finance, Elsevier, vol. 92(C), pages 909-928.
    2. Youtao Xiang & Sumuya Borjigin, 2024. "High–low volatility spillover network between economic policy uncertainty and commodity futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(8), pages 1295-1319, August.
    3. Ouyang, Zisheng & Zhou, Xuewei & Lu, Min & Liu, Ke, 2024. "Imported financial risk in global stock markets: Evidence from the interconnected network," Research in International Business and Finance, Elsevier, vol. 69(C).
    4. Yang, Xin & Wang, Xuya & Cao, Jie & Zhao, Lili & Huang, Chuangxia, 2024. "Cross-regional connectedness of financial market: Measurement and determinants," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).
    5. Ke, Rui & Shen, Anni & Yin, Man & Tan, Changchun, 2024. "The cross-sector risk contagion among Chinese financial institutions: Evidence from the extreme volatility spillover perspective," Finance Research Letters, Elsevier, vol. 63(C).

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    More about this item

    Keywords

    Interconnected networks; Extreme risk connectedness; Financial sector; Real estate sector;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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