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Sensitivity of Bond Portfolio's Behavior with Respect to Random Movements in Yield Curve: A Simulation Study

Author

Listed:
  • Marida Bertocchi
  • Vittorio Moriggia
  • Jitka Dupačová

Abstract

The bond portfolio management problem is formulated as a stochastic program based on interest rate scenarios. The coefficients of the resulting program are subject to errors of various kind. In this paper, we complement the theoretical stability results of by simulation experiments. Adapting the approach of to problems based on perturbed yield curves, we then provide bounds for the optimality gap for various candidate first-stage solutions. Copyright Kluwer Academic Publishers 2000

Suggested Citation

  • Marida Bertocchi & Vittorio Moriggia & Jitka Dupačová, 2000. "Sensitivity of Bond Portfolio's Behavior with Respect to Random Movements in Yield Curve: A Simulation Study," Annals of Operations Research, Springer, vol. 99(1), pages 267-286, December.
    • Handle: RePEc:spr:annopr:v:99:y:2000:i:1:p:267-286:10.1023/a:1019227901758
    DOI: 10.1023/A:1019227901758
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    Citations

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    Cited by:

    1. Lapshin, Victor & Sohatskaya, Sofia, 2020. "Choosing the weighting coefficients for estimating the term structure from sovereign bonds," International Review of Economics & Finance, Elsevier, vol. 70(C), pages 635-648.
    2. Rebecca Stockbridge & Güzin Bayraksan, 2016. "Variance reduction in Monte Carlo sampling-based optimality gap estimators for two-stage stochastic linear programming," Computational Optimization and Applications, Springer, vol. 64(2), pages 407-431, June.
    3. Dana Cíchová Králová, 2015. "Využití modelu BGM při řízení úrokového rizika v českém prostředí v období po finanční krizi [Aplication of the BGM Model for Interest Rate Risk Management in the Czech Environment after Financial ," Politická ekonomie, Prague University of Economics and Business, vol. 2015(6), pages 714-740.
    4. Yonghan Feng & Sarah Ryan, 2016. "Solution sensitivity-based scenario reduction for stochastic unit commitment," Computational Management Science, Springer, vol. 13(1), pages 29-62, January.
    5. Emrah Ahi & Vedat Akgiray & Emrah Sener, 2018. "Robust term structure estimation in developed and emerging markets," Annals of Operations Research, Springer, vol. 260(1), pages 23-49, January.
    6. Marida Bertocchi & Vittorio Moriggia & Jitka Dupačová, 2006. "Horizon and stages in applications of stochastic programming in finance," Annals of Operations Research, Springer, vol. 142(1), pages 63-78, February.
    7. Robert Ferstl & Alex Weissensteiner, 2010. "Backtesting short-term treasury management strategies based on multi-stage stochastic programming," Journal of Asset Management, Palgrave Macmillan, vol. 11(2), pages 94-112, June.

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