Peiwan Wang
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.
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Index Terms
- Predicting Chinese Bond Market Turbulences: Attention-BiLSTM Based Early Warning System
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