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On the predictive power of network statistics for financial risk indicators

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  • Song, Jianhua
  • Zhang, Zhepei
  • So, Mike K.P.

Abstract

An understanding of the financial instability during financial crises is an important topic in risk management. Market participants actively use risk indicators, such as the VIX in the US, the VHSI in Hong Kong and the V2TX in Europe, which are derived from derivative products, to measure market anxiety and fear and thus to estimate systemic risk in the market. In this paper, we present the findings of a study on the lead–lag relationship between financial connectedness and risk indicators. Specifically, we examine the predictive power of time-varying network statistics, compiled from more than 1300 stocks from international stock markets, on the risk indicators. Our empirical findings show strong evidence in favor of using network statistics to predict risk indicators. The findings reveal the importance of network connectedness in measuring systemic risk.

Suggested Citation

  • Song, Jianhua & Zhang, Zhepei & So, Mike K.P., 2021. "On the predictive power of network statistics for financial risk indicators," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 75(C).
    • Handle: RePEc:eee:intfin:v:75:y:2021:i:c:s1042443121001347
    DOI: 10.1016/j.intfin.2021.101420
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    3. Chen, Yan & Wang, Gang-Jin & Zhu, You & Xie, Chi & Uddin, Gazi Salah, 2023. "Quantile connectedness and the determinants between FinTech and traditional financial institutions: Evidence from China," Global Finance Journal, Elsevier, vol. 58(C).

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