Skip to main content

The Role of Asian Credit Default Swap Index in Portfolio Risk Management

  • Chapter
  • First Online:
Robustness in Econometrics

Part of the book series: Studies in Computational Intelligence ((SCI,volume 692))

Abstract

This paper aims at evaluating the performance of Asian Credit Default Swap (CDS) index in risk measurement and portfolio optimization by using several multivariate copulas-GARCH models with Expected Shortfall and Sharpe ratio. Multivariate copula-GARCH models consider the volatility and dependence structures of financial assets so that they are conductive to accurately predict risk and optimal portfolio. We find that vine copulas have better performance than other multivariate copulas in model estimation, while the multivariate T copulas have better performance than other kinds of copulas in risk measurement and portfolio optimization. Therefore, the model estimation, risk measurement, and portfolio optimization in empirical study should use different copula models. More importantly, the empirical results give evidences that Asian CDS index can reduce risk.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Amato JD (2005) Risk aversion and risk premia in the CDS market. BIS Q Rev

    Google Scholar 

  2. Oh DH, Patton AJ (2016) Time-varying systemic risk: evidence from a dynamic copula model of CDS spreads. J Bus Econ Stat (just-accepted):1–47

    Google Scholar 

  3. Schönbucher P (2005) Portfolio losses and the term structure of loss transition rates: a new methodology for the pricing of portfolio credit derivatives. Working paper

    Google Scholar 

  4. Kiesel R, Scherer M (2007) Dynamic credit portfolio modelling in structural models with jumps. Preprint, Universität Ulm

    Google Scholar 

  5. Kim DH, Loretan M, Remolona EM (2010) Contagion and risk premia in the amplification of crisis: evidence from Asian names in the global CDS market. J Asian Econ 21(3):314–326

    Article  Google Scholar 

  6. Pedersen CM (2003) Valuation of portfolio credit default swaptions. Lehman Brothers Quantitative Credit Research

    Google Scholar 

  7. Bo L, Capponi A (2014) Optimal investment in credit derivatives portfolio under contagion risk. Math Finan

    Google Scholar 

  8. Raunig B, Scheicher M (2008) A value at risk analysis of credit default swaps

    Google Scholar 

  9. Hürlimann W (2004) Multivariate Fréchet copulas and conditional value-at-risk. Int J Math Math Sci 2004(7):345–364

    Article  MATH  Google Scholar 

  10. Wei YH, Zhang SY (2007) Multivariate Copula-GARCH model and its applications in financial risk analysis. Appl Stat Manage 3:008

    Google Scholar 

  11. He X, Gong P (2009) Measuring the coupled risks: a copula-based CVaR model. J Comput Appl Math 223(2):1066–1080

    Article  MathSciNet  MATH  Google Scholar 

  12. Wang ZR, Chen XH, Jin YB, Zhou YJ (2010) Estimating risk of foreign exchange portfolio: using VaR and CVaR based on GARCHEVT-Copula model. Physica A Stat Mech Appl 389(21):4918–4928

    Article  Google Scholar 

  13. Emmanouil KN, Nikos N (2012) Extreme value theory and mixed canonical vine Copulas on modelling energy price risks. Working paper

    Google Scholar 

  14. Weiß GN, Supper H (2013) Forecasting liquidity-adjusted intraday Value-at-Risk with vine copulas. J Bank Finan 37(9):3334–3350

    Article  Google Scholar 

  15. Sriboonchitta S, Liu J, Kreinovich V, Nguyen HT (2014) A vine copula approach for analyzing financial risk and co-movement of the Indonesian, Philippine and Thailand stock markets. In: Modeling dependence in econometrics. Springer International Publishing, pp 245–257

    Google Scholar 

  16. Zhang B, Wei Y, Yu J, Lai X, Peng Z (2014) Forecasting VaR and ES of stock index portfolio: a vine copula method. Physica A Stat Mech Appl 416:112–124

    Article  MathSciNet  Google Scholar 

  17. Guegan D, Maugis, PA (2010) An econometric study of vine copulas. SSRN 1590296

    Google Scholar 

  18. Low RKY, Alcock J, Faff R, Brailsford T (2013) Canonical vine copulas in the context of modern portfolio management: are they worth it? J Bank Finan 37(8):3085–3099

    Article  Google Scholar 

  19. Bollerslev T (1986) Generalized autoregressive conditional heteroskedasticity. J Econometrics 31(3):307–327

    Article  MathSciNet  MATH  Google Scholar 

  20. Von Rohr P, Hoeschele I (2002) Bayesian QTL mapping using skewed Student-t distributions. Genetics Selection Evolution 34(1):1

    Article  Google Scholar 

  21. Wu CC, Chung H, Chang YH (2012) The economic value of co-movement between oil price and exchange rate using copula-based GARCH models. Energy Econ 34(1):270–282

    Article  Google Scholar 

  22. Bedford T, Cooke RM (2001) Monte Carlo simulation of vine dependent random variables for applications in uncertainty analysis. In: 2001 Proceedings of ESREL 2001, Turin, Italy

    Google Scholar 

  23. Bedford T, Cooke RM (2002) Vines-a new graphical model for dependent random variables. Ann Stat 30(4):10311068

    MathSciNet  MATH  Google Scholar 

  24. Aas K, Czado C, Frigessi A, Bakken H (2009) Pair-copula construction of multiple dependence. Insur Math Econ 44:182198

    Article  MathSciNet  MATH  Google Scholar 

  25. Brechmann EC, Czado C, Paterlini S (2014) Flexible dependence modeling of operational risk losses and its impact on total capital requirements. J Bank Finan 40:271285

    Article  Google Scholar 

  26. Joe H (2005) Asymptotic efficiency of the two-stage estimation method for copula based models. J Multivar Anal 94:401419

    Article  MathSciNet  Google Scholar 

  27. Rockafellar RT, Uryasev S (2002) Conditional value-at-risk for general loss distributions. J Bank Finan 26(7):1443–1471

    Article  Google Scholar 

  28. Liu J, Sriboonchitta S, Phochanachan P, Tang J (2015) Volatility and dependence for systemic risk measurement of the international financial system. Lecture notes in artificial intelligence (Subseries of Lecture notes in computer science), vol 9376. Springer, Heidelberg, pp 403–414

    Google Scholar 

  29. Kupiec P (1995) Techniques for verifying the accuracy of risk measurement models. J Deriv 3:7384

    Article  Google Scholar 

Download references

Acknowledgements

The financial support from the Puay Ungphakorn Centre of Excellence in Econometrics is greatly acknowledged. We would also like to express our gratitude to the many colleagues with whom, through the years, we have had the pleasure of discussing ideas on copulas and their applications.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jianxu Liu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Liu, J., Khiewngamdee, C., Sriboonchitta, S. (2017). The Role of Asian Credit Default Swap Index in Portfolio Risk Management. In: Kreinovich, V., Sriboonchitta, S., Huynh, VN. (eds) Robustness in Econometrics. Studies in Computational Intelligence, vol 692. Springer, Cham. https://doi.org/10.1007/978-3-319-50742-2_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-50742-2_26

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50741-5

  • Online ISBN: 978-3-319-50742-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics