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Tracking bond indices in an integrated market and credit risk environment

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  • Norbert Jobst
  • Stavros Zenios

Abstract

The management of credit risky assets requires simulation models that integrate the disparate sources of credit and market risk, and suitable optimization models for scenario analysis. In this paper we integrate Monte Carlo simulation models for credit risk with scenario optimization, and develop a methodology for tracking broadly defined corporate bond indices. Testing of the models shows that the integration of the multiple risk factors improves significantly the performance of tracking models. Good tracking performance can be achieved by optimizing strategic asset allocation among broad classes of corporate bonds. However, extra value is generated with a tactical model that optimizes bond picking decisions as well. It is also shown that adding small corporate bond holdings in portfolios that track government bond indices improves the risk/return characteristics of the portfolios. The empirical results to substantiate the findings of this study are obtained by backtesting the model over a recent 30 month period.

Suggested Citation

  • Norbert Jobst & Stavros Zenios, 2003. "Tracking bond indices in an integrated market and credit risk environment," Quantitative Finance, Taylor & Francis Journals, vol. 3(2), pages 117-135.
    • Handle: RePEc:taf:quantf:v:3:y:2003:i:2:p:117-135
    DOI: 10.1088/1469-7688/3/2/306
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    Citations

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    Cited by:

    1. Williams, Daniel G., 2007. "Scenario simulations do not yield results stochastically consistent with alternative Monte Carlo results: U.S. nuclear plant decommissioning funding adequacy (2000)," Energy Economics, Elsevier, vol. 29(5), pages 1101-1130, September.
    2. Grundke, Peter, 2010. "Top-down approaches for integrated risk management: How accurate are they?," European Journal of Operational Research, Elsevier, vol. 203(3), pages 662-672, June.
    3. Mitra, Sovan & Lim, Sungmook & Karathanasopoulos, Andreas, 2019. "Regression based scenario generation: Applications for performance management," Operations Research Perspectives, Elsevier, vol. 6(C).
    4. Jobst, Norbert J. & Zenios, Stavros A., 2005. "On the simulation of portfolios of interest rate and credit risk sensitive securities," European Journal of Operational Research, Elsevier, vol. 161(2), pages 298-324, March.
    5. Jobst, Norbert J. & Mitra, Gautam & Zenios, Stavros A., 2006. "Integrating market and credit risk: A simulation and optimisation perspective," Journal of Banking & Finance, Elsevier, vol. 30(2), pages 717-742, February.
    6. Topaloglou, Nikolas & Vladimirou, Hercules & Zenios, Stavros A., 2020. "Integrated dynamic models for hedging international portfolio risks," European Journal of Operational Research, Elsevier, vol. 285(1), pages 48-65.

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