Systemic Risk Measurement and Its Economic Early Warning Ability: Based on Mixed-Frequency Dynamic Factor Model ()
Affiliation(s)
1International Business College, Dongbei University of Finance and Economics, Dalian, China.
2Hunan Key Laboratory of Macroeconomic Big Data Mining and Its Application, Changsha, China.
3School of Business, Hunan Normal University, Changsha, China.
4School of Finance, Hunan University of Finance and Economics, Changsha, China.
ABSTRACT
As China’s participation in the global market intensifies, the systemic
risk arising from its expansive and interconnected economy becomes increasingly
significant worldwide. The inherent complexity of systemic risk necessitates
the integration of a wide array of information sources for its accurate
assessment. In this context, our study utilizes the mixed-frequency dynamic
factor model to develop a Systemic Risk Index (SRI) that effectively
encapsulates. This model is adept at merging data indicators from varying frequencies,
which is crucial for capturing the multifaceted nature of systemic risk.
Moreover, the study further delves into the macroeconomic early warning
capabilities of the SRI. Our findings demonstrate that the SRI is proficient in
integrating and distilling information from diverse market dimensions, offering
a more nuanced representation of China’s economic and financial risks.
Moreover, the SRI exhibits a robust capacity for economic foresight, outpacing
macroeconomic indicators by a minimum of 12 months.
Share and Cite:
Li, J. , Yang, Y. and Zhou, L. (2024) Systemic Risk Measurement and Its Economic Early Warning Ability: Based on Mixed-Frequency Dynamic Factor Model.
Theoretical Economics Letters,
14, 164-183. doi:
10.4236/tel.2024.141009.
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