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Wavelets Analysis on Structural Model for Default Prediction

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  • Lu Han

    (Central University of Finance & Economics)

  • Ruihuan Ge

    (Central University of Finance & Economics)

Abstract

In recent years, to improve predictive ability of corporate defaults has become an important problem. In this paper, regarding on characteristics of listed companies, we sampled 100 companies according to industry types, constructed wavelet structural model, experimented with wavelet decomposition proceeds to get low frequency and high frequency sequence, built the prediction model for both sequences, and then using the prediction of future returns to reconstruct predictive returns, thus avoiding accumulated prediction process with earnings volatility of time series model, therefore enhanced the precision of default prediction. Finally we compared wavelet structural model with time series structural model based on the predictive default distance of China’s listed companies.

Suggested Citation

  • Lu Han & Ruihuan Ge, 2017. "Wavelets Analysis on Structural Model for Default Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 50(1), pages 111-140, June.
    • Handle: RePEc:kap:compec:v:50:y:2017:i:1:d:10.1007_s10614-016-9584-1
    DOI: 10.1007/s10614-016-9584-1
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    References listed on IDEAS

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    Cited by:

    1. Zhang, Xuan & Zhao, Yang & Yao, Xiao, 2022. "Forecasting corporate default risk in China," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1054-1070.
    2. Indranil Ghosh & Manas K. Sanyal & R. K. Jana, 2021. "Co-movement and Dynamic Correlation of Financial and Energy Markets: An Integrated Framework of Nonlinear Dynamics, Wavelet Analysis and DCC-GARCH," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 503-527, February.
    3. Yi-Ting Chen & Wan-Ni Lai & Edward W. Sun, 2019. "Jump Detection and Noise Separation by a Singular Wavelet Method for Predictive Analytics of High-Frequency Data," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 809-844, August.

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