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Measuring systemic risk with high-frequency data: A realized GARCH approach

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  • Chen, Qihao
  • Huang, Zhuo
  • Liang, Fang

Abstract

This paper incorporates high-frequency information to measure systemic risk. Under the Multivariate Realized GARCH framework, we compute the CoVaR measure using a multivariate skew-t distribution. Using 5-minute data of 98 U.S. financial institutions from 2000 to 2022, we show the empirical improvement of the high-frequency measurement. We also investigate the relationship between institutions’ systemic risk contributions and firm-level characteristics. Our empirical findings suggest that firm size and leverage are positively related to institutions’ contributions to systemic risk.

Suggested Citation

  • Chen, Qihao & Huang, Zhuo & Liang, Fang, 2023. "Measuring systemic risk with high-frequency data: A realized GARCH approach," Finance Research Letters, Elsevier, vol. 54(C).
    • Handle: RePEc:eee:finlet:v:54:y:2023:i:c:s1544612323001265
    DOI: 10.1016/j.frl.2023.103753
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    Cited by:

    1. Xiaoyang Chen & Liguo Zhou & Lin Wang & Yuelong Zheng, 2023. "Risk spillover in China’s real estate industry chain: a DCC-EGARCH-ΔCoVaR model," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-16, December.

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    More about this item

    Keywords

    Systemic risk; CoVaR; Multivariate realized GARCH; Multivariate skew-t distribution;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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