Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Modeling default risk via a hidden Markov model of multiple sequences

  • Research Article
  • Published:
Frontiers of Computer Science in China Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Li D. On default correlation: a Copula function approach. Journal of Fixed Income, 2000, 9(4): 43–54

    Article  Google Scholar 

  2. McNeil A, Frey R, Embrechts P. Quantitative risk management: concepts, techniques and tools. Princeton University Press, 2005

  3. Credit Suisse Financial Products. Credit Risk+ a Credit Risk Management Framework, 1997, http://www.csfb.com/institutional/research/creditrisk.html

  4. Duffie D, Eckner A, Horel G, Saita L. Frailty correlated default, Graduate School of Business, Stanford University, 2006, Preprint

  5. Das S, Duffie D, Kapadia N, Saita L. Common failings: how corporate defaults are correlated? Journal of Finance, 2007, 62(1): 93–117

    Article  Google Scholar 

  6. Moody’s Investment Services. The Binomial Expansion Method Applied to CBO/CLO Analysis. 1999

  7. Davis M, Lo V. Infectious defaults. Quantitative Finance, 2001, 1(4): 382–387

    Article  Google Scholar 

  8. Davis M, Lo V. Modeling default correlation in bond portfolio. In: Alescander C, ed. Mastering Risk Volume 2: Applications. Financial Times, Prentice Hall, 2001, 141–151

  9. Giampieri G, Davis M, Crowder M. Analysis of default data using hidden Markov models. Quantitative Finance, 2005, 5(1): 27–34

    Article  MATH  MathSciNet  Google Scholar 

  10. MacDonald I, Zucchini W. Hidden Markov and Other Models for Discrete-Valued Time Series. London:. Chapman & Hall, 1999

    Google Scholar 

  11. Rabiner L R. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 1989, 77(2): 257–286

    Article  Google Scholar 

  12. Ching W, Ng M. Markov Chains: Models, Algorithms and Applications. International Series on Operations Research and Management Science, New York: Springer, 2006

    Google Scholar 

  13. Ching W, Ng M, Wong K. Hidden Markov models and thier applications to customer relationship management. IMA Journal of Management Mathematics, 2004, 15(1): 13–24

    Article  MATH  MathSciNet  Google Scholar 

  14. Ching W, Fung E, Ng M, Siu T, Li W. Interactive hidden Markov models and their applications. IMA Journal of Management Mathematics, 2007, 18(1): 85–97

    Article  MATH  MathSciNet  Google Scholar 

  15. Ching W, Siu T, Li L, Li T, Li W. Modeling default data via an interactive hidden Markov model, Computational Economics. Computational Economics, 2009, 34(1): 1–19

    Article  MATH  MathSciNet  Google Scholar 

  16. Ching W K, Leung H, Jiang H, Wu Z. Hidden Markov models for default risk. In: Proceedings of the 2nd International Symposium on Financial Information Processing (FIP), Beijing, China, 2009

  17. Levinson S, Rabiner L, Sondhi M. An introduction to the application if theory of probabilistic functions of Markov process to automatic speech recognition. Bell System Technical Journal, 1983, 62: 1035–1074

    MATH  MathSciNet  Google Scholar 

  18. Li X, Parizeau M, Plamondon R. Training hidden Markov models with multiple observation- a combinatorial method. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(4): 371–377

    Article  Google Scholar 

  19. Zhang D, Shi Y, Tian Y, Zhu M. A class of classification and regression methods by multiobjective programming. Frontiers of Computer Science in China, 2009, 3(2): 192–204

    Article  Google Scholar 

  20. Zhou L, Lai K. Benchmarking binary classification models on data sets with different degrees of imbalance. Frontiers of Computer Science in China, 2009, 3(2): 205–216

    Article  MathSciNet  Google Scholar 

  21. Richard A, Johnson, Dean W. Wichern. Applied Multivariate Statistical Analysis. Prentice Hall International Series, U.S. 1992, 553

  22. Siu T K, Ching W K, Fung E S, Ng M K. Extracting information from spot interest rates and credit ratings using double higher-order hidden Markov models. Computational Economics, 2005, 26(3): 69–102

    Article  Google Scholar 

Download references

Author information

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

Authors
  1. Wai-Ki Ching
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ho-Yin Leung
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhenyu Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hao Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wai-Ki Ching.

Rights and permissions

Reprints and permissions

About this article

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

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

Keywords

Search

Navigation