Predicting Chinese Bond Market Turbulences: Attention-BiLSTM Based Early Warning System

Peiwan Wang, Lu Zong, Yurun Yang

Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review

1 Citation (Scopus)

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 languageEnglish
Title of host publicationProceedings of the 2020 2nd International Conference on Big Data Engineering
Publisher Association for Computing Machinery
Pages91-104
Number of pages14
ISBN (Print)9781450377225
DOIs
Publication statusPublished - 5 Jul 2020
Externally publishedYes
Event2020 2nd International Conference on Big Data Engineering - Shanghai , China
Duration: 29 May 202031 May 2020
http://www.bde.net/

Conference

Conference2020 2nd International Conference on Big Data Engineering
Abbreviated titleBDE 2020
Country/TerritoryChina
CityShanghai
Period29/05/2031/05/20
Internet address

Bibliographical note

© 2020 Association for Computing Machinery

Keywords

  • Volatility classified crisis
  • Attention mechanism
  • Early warning system
  • Deep neural networks
  • Regime switching ARCH

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