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Systematic Risk Stress Prediction in Bond Market Based on EEMD-LSTM

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DOI: 10.23977/ferm.2023.061126 | Downloads: 5 | Views: 267


Zongxuan Chai 1, Tingting Zheng 1


1 School of Electrical and Control Engineering, North China University of Technology, Beijing, 100144, China

Corresponding Author

Tingting Zheng


Forecasting financial systemic risk has always been an important element of financial research. Despite being a key component of our country's financial market, the bond market has received relatively less systematic research attention from scholars in terms of singular market risk warnings. This paper draws on established research to construct a systemic risk stress index for the bond market. Innovatively, it utilizes the Empirical Mode Decomposition (EEMD) method to decompose the pressure index of the Chinese bond market from 2010 to 2023 into individual IMF sequences. Then, it employs the LSTM algorithm for ensemble forecasting, conducting systematic risk warning research. According to the simulation results, China's bond market will show a trend of declining pressure or low pressure in the long term, and the systemic risk will fluctuate less under effective regulation. Meanwhile, the EEMD-LSTM model has higher risk prediction accuracy compared with single LSTM model prediction.


Systemic risk; Bond Markets; Ensemble Empirical Mode Decomposition; Long Short-Term Memory


Zongxuan Chai, Tingting Zheng, Systematic Risk Stress Prediction in Bond Market Based on EEMD-LSTM. Financial Engineering and Risk Management (2023) Vol. 6: 181-186. DOI:


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