Measuring systemic risk in China: A new hybrid approach incorporating ensemble learning and risk spillover networks

Da Huo, Yongdong Shi, Chao Wang, Lihan Wang, Weize Xing, Mo Yang and Jingjing Zhao

Pacific-Basin Finance Journal, 2025, vol. 91, issue C

Abstract: To address the limitations of traditional systemic risk indices in measuring nonlinearity and network interdependence, we introduce ESRISK, a novel systemic risk measure that incorporates ensemble learning and risk spillover networks. Our approach can effectively analyze the complex nonlinearity in high-dimensional data, enabling more accurate quantification of systemic risk in China's financial system. Comprehensive evaluations reveal that ESRISK outperforms prevailing systemic risk measures, particularly in predictability, accuracy in measuring systemic risk, and effectiveness in early warning detection of systemic events. Moreover, ESRISK demonstrates superior predictive power for macroeconomic downturns. Our findings highlight the importance of applying machine learning methods and considering inter-institutional spillovers when measuring systemic risk in China's financial ecosystem.

Keywords: Systemic risk measure; Ensemble learning; SRISK; Marginal expected shortfall; Risk spillover index (search for similar items in EconPapers)
JEL-codes: C5 G21 G28 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:pacfin:v:91:y:2025:i:c:s0927538x25001015

DOI: 10.1016/j.pacfin.2025.102764

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