Wavelets Analysis on Structural Model for Default Prediction

Lu Han () and Ruihuan Ge
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Lu Han: Central University of Finance & Economics
Ruihuan Ge: Central University of Finance & Economics

Computational Economics, 2017, vol. 50, issue 1, No 5, 140 pages

Abstract: 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.

Keywords: Wavelet structural model; Time series analysis; Default prediction; Credit risk management (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10614-016-9584-1

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