IDEAS home Printed from https://ideas.repec.org/a/kap/apfinm/v28y2021i4d10.1007_s10690-021-09336-6.html
   My bibliography  Save this article

Bankruptcy risk dependence structure using the INAR model comprising macroeconomic indicators applied to stress tests

Author

Listed:
  • Teruo Kemmotsu

    (Hitotsubashi University)

Abstract

In this study, we examined the types of bankruptcy risk dependence structures of Japanese firms. We classified bankruptcy events observed in Japan into multiple event types based on industry type and then used a multidimensional Hawkes process, which has been gaining attention recently in the field of finance research, to model the self-exciting and/or mutually exciting properties of bankruptcy among the event types. For the estimation of the intensity processes associated with the multidimensional Hawkes process, in addition to the analysis by Embrechts and Kirchner (Quantitative Finance 17:571–595, 2016) using the integer-valued auto-regression (INAR) model, we tested a new approach using the INAR model comprising macroeconomic indicators. Subsequently, we compared and considered the estimation results of the two specifications and demonstrated that the INAR model comprising macroeconomic indicators is better than the INAR model lacking macroeconomic indicators in terms of estimating the intensity process. Further, we demonstrated the effectiveness of the model from a practical perspective, with applications to stress tests.

Suggested Citation

  • Teruo Kemmotsu, 2021. "Bankruptcy risk dependence structure using the INAR model comprising macroeconomic indicators applied to stress tests," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 28(4), pages 563-585, December.
    • Handle: RePEc:kap:apfinm:v:28:y:2021:i:4:d:10.1007_s10690-021-09336-6
    DOI: 10.1007/s10690-021-09336-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10690-021-09336-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10690-021-09336-6?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.

    References listed on IDEAS

    as
    1. Emmanuel Bacry & Iacopo Mastromatteo & Jean-Franc{c}ois Muzy, 2015. "Hawkes processes in finance," Papers 1502.04592, arXiv.org, revised May 2015.
    2. Azizpour, S & Giesecke, K. & Schwenkler, G., 2018. "Exploring the sources of default clustering," Journal of Financial Economics, Elsevier, vol. 129(1), pages 154-183.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kramer, Anke & Kiesel, Rüdiger, 2021. "Exogenous factors for order arrivals on the intraday electricity market," Energy Economics, Elsevier, vol. 97(C).
    2. Yang Shen & Bin Zou, 2021. "Mean-Variance Portfolio Selection in Contagious Markets," Papers 2110.09417, arXiv.org.
    3. Rachele Foschi & Francesca Lilla & Cecilia Mancini, 2020. "Warnings about future jumps: properties of the exponential Hawkes model," Working Papers 13/2020, University of Verona, Department of Economics.
    4. Peng Wu & Marcello Rambaldi & Jean-Franc{c}ois Muzy & Emmanuel Bacry, 2019. "Queue-reactive Hawkes models for the order flow," Papers 1901.08938, arXiv.org.
    5. Marcello Rambaldi & Emmanuel Bacry & Jean-Franc{c}ois Muzy, 2018. "Disentangling and quantifying market participant volatility contributions," Papers 1807.07036, arXiv.org.
    6. Kyungsub Lee, 2022. "Application of Hawkes volatility in the observation of filtered high-frequency price process in tick structures," Papers 2207.05939, arXiv.org, revised Sep 2024.
    7. Anna Dubinova & Andre Lucas & Sean Telg, 2021. "COVID-19, Credit Risk and Macro Fundamentals," Tinbergen Institute Discussion Papers 21-059/III, Tinbergen Institute.
    8. Anatoliy Swishchuk & Aiden Huffman, 2020. "General Compound Hawkes Processes in Limit Order Books," Risks, MDPI, vol. 8(1), pages 1-25, March.
    9. Michi Nishihara & Takashi Shibata, 2020. "Optimal capital structure and bankruptcy cascades," Discussion Papers in Economics and Business 20-10, Osaka University, Graduate School of Economics.
    10. Ivan Jericevich & Patrick Chang & Tim Gebbie, 2021. "Simulation and estimation of a point-process market-model with a matching engine," Papers 2105.02211, arXiv.org, revised Aug 2021.
    11. Alain Monfort & Fulvio Pegoraro & Jean-Paul Renne & Guillaume Roussellet, 2021. "Affine Modeling of Credit Risk, Pricing of Credit Events, and Contagion," Management Science, INFORMS, vol. 67(6), pages 3674-3693, June.
    12. Guanxing Fu & Ulrich Horst & Xiaonyu Xia, 2022. "Portfolio liquidation games with self‐exciting order flow," Mathematical Finance, Wiley Blackwell, vol. 32(4), pages 1020-1065, October.
    13. Asis, Gonzalo & Chari, Anusha & Haas, Adam, 2021. "In search of distress risk in emerging markets," Journal of International Economics, Elsevier, vol. 131(C).
    14. Zhao, Yixiu & Upreti, Vineet & Cai, Yuzhi, 2021. "Stock returns, quantile autocorrelation, and volatility forecasting," International Review of Financial Analysis, Elsevier, vol. 73(C).
    15. Ulrich Horst & Wei Xu, 2019. "Functional Limit Theorems for Marked Hawkes Point Measures ," Working Papers hal-02443841, HAL.
    16. Patrick Chang & Roger Bukuru & Tim Gebbie, 2019. "Revisiting the Epps effect using volume time averaging: An exercise in R," Papers 1912.02416, arXiv.org, revised Feb 2020.
    17. Harjoat S. Bhamra & Christian Dorion & Alexandre Jeanneret & Michael Weber, 2018. "Low Inflation: High Default Risk AND High Equity Valuations," NBER Working Papers 25317, National Bureau of Economic Research, Inc.
    18. Mercuri, Lorenzo & Perchiazzo, Andrea & Rroji, Edit, 2024. "A Hawkes model with CARMA(p,q) intensity," Insurance: Mathematics and Economics, Elsevier, vol. 116(C), pages 1-26.
    19. Paul Jusselin & Mathieu Rosenbaum, 2020. "No‐arbitrage implies power‐law market impact and rough volatility," Mathematical Finance, Wiley Blackwell, vol. 30(4), pages 1309-1336, October.
    20. Chernov, Mikhail & Augustin, Patrick & Song, Dongho, 2018. "Sovereign credit risk and exchange rates: Evidence from CDS quanto spreads," CEPR Discussion Papers 12857, C.E.P.R. Discussion Papers.

    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:kap:apfinm:v:28:y:2021:i:4:d:10.1007_s10690-021-09336-6. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.