Systemic risk prediction based on Savitzky-Golay smoothing and temporal convolutional networks

Xite Yang; Ankang Zou; Jidi Cao; Yongzeng Lai; Jilin Zhang; Xite Yang; Ankang Zou; Jidi Cao; Yongzeng Lai; Jilin Zhang

doi:10.3934/era.2023135

Electronic Research Archive

2023, Volume 31, Issue 5: 2667-2688. doi: 10.3934/era.2023135

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Research article

Systemic risk prediction based on Savitzky-Golay smoothing and temporal convolutional networks

1.
Business School, Sichuan University, Chengdu, China
2.
National Engineering Research Center for Big Data Software, Tsinghua University, Beijing, China
3.
Department of Mathematics, Wilfrid Laurier University, Ontario, Canada
4.
School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou, China

Received: 14 January 2023 Revised: 15 February 2023 Accepted: 23 February 2023 Published: 09 March 2023

Based on the data from January 2007 to December 2021, this paper selects 14 representatives from four levels of the extreme risk of financial institutions, the contagion effect between financial systems, volatility and instability of financial markets, liquidity, and credit risk systemic risk. By constructing a Savitzky-Golay-TCN deep convolutional neural network, the systemic risk indicators of China's financial market are predicted, and their accuracy and reliability are analyzed. The research found that: 1) Savitzky-Golay-TCN deep convolutional neural network has a strong generalization ability, and the prediction effect on all indices is stable. 2) Compared with the three control models (time-series convolutional network (TCN), convolutional neural network (CNN), and long short-term memory (LSTM)), the Savitzky-Golay-TCN deep convolutional neural network has excellent prediction accuracy, and its average prediction accuracy for all indices has increased. 3) Savitzky-Golay-TCN deep convolutional neural network can better monitor financial market changes and effectively predict systemic risk.
- financial market,
- systemic risk forecasting,
- deep learning,
- Savitzky-Golay-TCN neural network model
Citation: Xite Yang, Ankang Zou, Jidi Cao, Yongzeng Lai, Jilin Zhang. Systemic risk prediction based on Savitzky-Golay smoothing and temporal convolutional networks[J]. Electronic Research Archive, 2023, 31(5): 2667-2688. doi: 10.3934/era.2023135

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Abstract

Based on the data from January 2007 to December 2021, this paper selects 14 representatives from four levels of the extreme risk of financial institutions, the contagion effect between financial systems, volatility and instability of financial markets, liquidity, and credit risk systemic risk. By constructing a Savitzky-Golay-TCN deep convolutional neural network, the systemic risk indicators of China's financial market are predicted, and their accuracy and reliability are analyzed. The research found that: 1) Savitzky-Golay-TCN deep convolutional neural network has a strong generalization ability, and the prediction effect on all indices is stable. 2) Compared with the three control models (time-series convolutional network (TCN), convolutional neural network (CNN), and long short-term memory (LSTM)), the Savitzky-Golay-TCN deep convolutional neural network has excellent prediction accuracy, and its average prediction accuracy for all indices has increased. 3) Savitzky-Golay-TCN deep convolutional neural network can better monitor financial market changes and effectively predict systemic risk.

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