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

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  • 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
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    References listed on IDEAS

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    1. Azizpour, S & Giesecke, K. & Schwenkler, G., 2018. "Exploring the sources of default clustering," Journal of Financial Economics, Elsevier, vol. 129(1), pages 154-183.
    2. Emmanuel Bacry & Iacopo Mastromatteo & Jean-Franc{c}ois Muzy, 2015. "Hawkes processes in finance," Papers 1502.04592, arXiv.org, revised May 2015.
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