Abstract
The study aims to construct an effective early warning system (EWS) to predict the crisis triggered turbulence in Chinese bond market by integrating the volatility regime switching model, SWARCH, to improve the crisis classifying precision, and the stylized predictive model, Attention-BiLSTM of attention mechanism based deep neural networks, to resolve the predicting hysteresis. The model versatility and comparability are investigated and testified by applying multiple prominent EWS models to bonds with different credit rating levels. The hybrid EWS also specifies the leading factors relating to the bond credit rating, that will practically instruct governors and market participants to focus on either the national economy associated or the corporate finance concerned factors according to the bond varying credit risks to make more effective predictions.
Original language | English |
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Title of host publication | Proceedings of the 2020 2nd International Conference on Big Data Engineering |
Publisher | Association for Computing Machinery |
Pages | 91-104 |
Number of pages | 14 |
ISBN (Print) | 9781450377225 |
DOIs | |
Publication status | Published - 5 Jul 2020 |
Externally published | Yes |
Event | 2020 2nd International Conference on Big Data Engineering - Shanghai , China Duration: 29 May 2020 → 31 May 2020 http://www.bde.net/ |
Conference
Conference | 2020 2nd International Conference on Big Data Engineering |
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Abbreviated title | BDE 2020 |
Country/Territory | China |
City | Shanghai |
Period | 29/05/20 → 31/05/20 |
Internet address |
Bibliographical note
© 2020 Association for Computing MachineryKeywords
- Volatility classified crisis
- Attention mechanism
- Early warning system
- Deep neural networks
- Regime switching ARCH