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Modeling default risk via a hidden Markov model of multiple sequences

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Abstract

Default risk in commercial lending is one of the major concerns of the creditors. In this article, we introduce a new hidden Markov model (HMM) with multiple observable sequences (MHMM), assuming that all the observable sequences are driven by a common hidden sequence, and utilize it to analyze default data in a network of sectors. Efficient estimation method is then adopted to estimate the model parameters. To further illustrate the advantages of MHMM, we compare the hidden risk state process obtained by MHMM with that from the traditional HMMs using credit default data. We then consider two applications of our MHMM. The calculation of two important risk measures: Value-at-risk (VaR) and expected shortfall (ES) and the prediction of global risk state. We first compare the performance of MHMM and HMM in the calculation of VaR and ES in a portfolio of default-prone bonds. A logistic regression model is then considered for the prediction of global economic risk using our MHMM with default data. Numerical results indicate our model is effective for both applications.

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Authors and Affiliations

  1. Advanced Modeling and Applied Computing Laboratory, Department of Mathematics, The University of Hong Kong, Hong Kong, China

    Wai-Ki Ching, Ho-Yin Leung & Hao Jiang

  2. Department of Finance and Management Science, N. Murray Edwards School of Business, University of Saskatchewan, Saskatoon, SK, S7N 5A7, Canada

    Zhenyu Wu

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  1. Wai-Ki Ching
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  2. Ho-Yin Leung
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  3. Zhenyu Wu
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  4. Hao Jiang
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Correspondence to Wai-Ki Ching.

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Ching, WK., Leung, HY., Wu, Z. et al. Modeling default risk via a hidden Markov model of multiple sequences. Front. Comput. Sci. China 4, 187–195 (2010). https://doi.org/10.1007/s11704-010-0501-9

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  • DOI: https://doi.org/10.1007/s11704-010-0501-9

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