IDEAS home Printed from https://ideas.repec.org/a/taf/apeclt/v28y2021i5p413-420.html
   My bibliography  Save this article

A hybrid model to estimate corporate default probabilities in China based on zero-price probability model and long short-term memory

Author

Listed:
  • Jiabao Jing
  • Wenwen Yan
  • Xiaomei Deng

Abstract

This article proposes a hybrid model by combining zero-price probability model with long short-term memory (ZPP-LSTM) to estimate corporate default probabilities. The ZPP-LSTM model enhances the time-series data forecast by introducing LSTM in ZPP model, which can better estimate the corporate default probabilities in the industry sensitive to an uncertain environment. The full samples of Chinese listed companies in construction and real estate industries are selected to evaluate the performance of ZPP-LSTM model. The results show that our proposed model outperforms other benchmark models in terms of the default probability estimation.

Suggested Citation

  • Jiabao Jing & Wenwen Yan & Xiaomei Deng, 2021. "A hybrid model to estimate corporate default probabilities in China based on zero-price probability model and long short-term memory," Applied Economics Letters, Taylor & Francis Journals, vol. 28(5), pages 413-420, March.
    • Handle: RePEc:taf:apeclt:v:28:y:2021:i:5:p:413-420
    DOI: 10.1080/13504851.2020.1757611
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/13504851.2020.1757611
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/13504851.2020.1757611?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dean Fantazzini, 2022. "Crypto-Coins and Credit Risk: Modelling and Forecasting Their Probability of Death," JRFM, MDPI, vol. 15(7), pages 1-34, July.
    2. Fantazzini, Dean, 2023. "Assessing the Credit Risk of Crypto-Assets Using Daily Range Volatility Models," MPRA Paper 117141, University Library of Munich, Germany.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:apeclt:v:28:y:2021:i:5:p:413-420. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RAEL20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.