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Downside and upside risk spillovers between financial industry and real economy based on linear and nonlinear networks

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  • Xiang, Youtao
  • Borjigin, Sumuya

Abstract

The spillover effects between financial sectors and real economy in different risk levels are of concern for investors and regulatory authorities. Firstly, based on the sector returns of China, the VaRs at different quantiles are estimated by MVMQ-CAViaR model. Then, we construct linear and nonlinear risk spillover networks in downside, normal and upside cases by employing DY and nonlinear Granger causality methods. Finally, we analyze topological characteristics of linear and nonlinear risk spillover networks at the system and sector levels. The results show that linear and nonlinear risk spillover networks in different risk levels exhibit various topology properties, they are unevenly spread over each risk level. At the system-level, we observe that there is a significant difference between linear and nonlinear risk spillover networks in uniqueness and overlapping. At the sector-level, the financial sectors (such as DF and RE sectors) can form a certain spillover effect on sectors of real economy across quantiles, but the net spillover effects of the financial sectors are smaller than that of some real economy sectors. Finally, the crisis shocks have impact on risk spillover effects between financial sectors and real economy. In downside and upside cases, the spillover effects between sectors during crisis period is higher than that during pre-crisis period.

Suggested Citation

  • Xiang, Youtao & Borjigin, Sumuya, 2023. "Downside and upside risk spillovers between financial industry and real economy based on linear and nonlinear networks," International Review of Economics & Finance, Elsevier, vol. 88(C), pages 1337-1374.
  • Handle: RePEc:eee:reveco:v:88:y:2023:i:c:p:1337-1374
    DOI: 10.1016/j.iref.2023.07.066
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    Cited by:

    1. Xiang, Youtao & Borjigin, Sumuya, 2024. "Multilayer networks for measuring interconnectedness among global stock markets through the lens of trading volume-price relationship," Global Finance Journal, Elsevier, vol. 62(C).
    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. Lin, Zi-Luo & Ouyang, Wen-Pei & Yu, Qing-Rui, 2024. "Risk spillover effects of the Israel–Hamas War on global financial and commodity markets: A time–frequency and network analysis," Finance Research Letters, Elsevier, vol. 66(C).
    4. Ouyang, Minhua & Xiao, Hailian, 2024. "Tail risk spillovers among Chinese stock market sectors," Finance Research Letters, Elsevier, vol. 62(PB).
    5. Yang, Jie & Feng, Yun, 2023. "Market inefficiency spillover network across different regimes," Finance Research Letters, Elsevier, vol. 58(PC).

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    More about this item

    Keywords

    Risk spillover network; MVMQ-CAViaR; Downside risk; Upside risk;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G20 - Financial Economics - - Financial Institutions and Services - - - General

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