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Portfolio selection strategy for fixed income markets with immunization on average

Author

Listed:
  • Sergio Ortobelli

    (University of Bergamo
    Technical University Ostrava)

  • Sebastiano Vitali

    (University of Bergamo)

  • Marco Cassader

    (University of Bergamo
    Technical University Ostrava)

  • Tomáš Tichý

    (Technical University Ostrava)

Abstract

In this paper, we develop a portfolio optimization method to maximize the performance of a fixed income portfolio. To achieve this aim, we define a two-step optimization problem where we firstly manage the immunization risk and then we maximize the portfolio wealth in a reward–risk framework. In the first optimization step, we create funds of bonds with constant immunization measure over time, and we propose an innovative immunization measure for bond portfolio management that leads to a more flexible immunization approach and a better trade-off between reward and risk. In the second optimization step, maximizing two performance measures on these baskets of bonds we obtain portfolio strategies that consider different investors’ profiles. An empirical application to the US fixed income market during 2002–2012 period is provided. Applying the portfolio optimization method to different bond classes, we compare the results with an equity index. This ex-post analysis indicates the benefits of the proposed portfolio strategy in outperforming the benchmark and proves that capital flows to safer markets during crisis periods.

Suggested Citation

  • Sergio Ortobelli & Sebastiano Vitali & Marco Cassader & Tomáš Tichý, 2018. "Portfolio selection strategy for fixed income markets with immunization on average," Annals of Operations Research, Springer, vol. 260(1), pages 395-415, January.
    • Handle: RePEc:spr:annopr:v:260:y:2018:i:1:d:10.1007_s10479-016-2182-8
    DOI: 10.1007/s10479-016-2182-8
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    References listed on IDEAS

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    Cited by:

    1. Victor Lapshin, 2021. "Immunizing a Marked-to-Model Obligation with Marked-to-Market Financial Instruments," HSE Working papers WP BRP 84/FE/2021, National Research University Higher School of Economics.
    2. Reza Bradrania & Davood Pirayesh Neghab, 2022. "State-dependent Asset Allocation Using Neural Networks," Papers 2211.00871, arXiv.org.
    3. Wulan Anggraeni & Sudradjat Supian & Sukono & Nurfadhlina Abdul Halim, 2023. "Catastrophe Bond Diversification Strategy Using Probabilistic–Possibilistic Bijective Transformation and Credibility Measures in Fuzzy Environment," Mathematics, MDPI, vol. 11(16), pages 1-30, August.
    4. Bradrania, Reza & Pirayesh Neghab, Davood, 2021. "State-dependent asset allocation using neural networks," MPRA Paper 115254, University Library of Munich, Germany.
    5. Sergio Ortobelli Lozza & Enrico Angelelli & Alda Ndoci, 2019. "Timing portfolio strategies with exponential Lévy processes," Computational Management Science, Springer, vol. 16(1), pages 97-127, February.
    6. Chinnadurai Kathiravan & Murugesan Selvam & Sankaran Venkateswar & S. Balakrishnan, 2021. "Investor behavior and weather factors: evidences from Asian region," Annals of Operations Research, Springer, vol. 299(1), pages 349-373, April.
    7. Steven James Lee, 2021. "Does Fixed Income Buffer against Fraud Shocks?," JRFM, MDPI, vol. 14(10), pages 1-22, October.

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