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Downside risk, financial conditions and systemic risk in China

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  • Wang, Bo
  • Li, Haoran

Abstract

Using quantile regression and Bayesian-VAR model, we demonstrate that deteriorating financial conditions and high systemic risk reinforce future downside risk when current GDP growth is relatively low in China. We construct the financial conditions indexes (FCIs) and find that there is a large difference between the forecasting ability of financial conditions and systemic risk to future GDP growth. The leading and early warning functions of systemic risks are better than that of financial conditions.

Suggested Citation

  • Wang, Bo & Li, Haoran, 2021. "Downside risk, financial conditions and systemic risk in China," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).
    • Handle: RePEc:eee:pacfin:v:68:y:2021:i:c:s0927538x19304895
    DOI: 10.1016/j.pacfin.2020.101356
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    Cited by:

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    3. Zhang, Yunhan & Ji, Qiang & Zhang, Dayong & Guo, Kun, 2024. "How does Shanghai crude oil futures affect top global oil companies: The role of multi-uncertainties," Energy Economics, Elsevier, vol. 131(C).
    4. Lin Zhu & Jian He, 2024. "China financial stability and asymmetric implications for economic stability," Economic Change and Restructuring, Springer, vol. 57(1), pages 1-29, February.

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    More about this item

    Keywords

    Downside risk; Financial conditions; Systemic risk; Quantile regression; BVAR;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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