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Portfolio optimization of credit risky bonds: a semi-Markov process approach

Author

Listed:
  • Puneet Pasricha

    (Department of Mathematics, Indian Institute of Technology Delhi)

  • Dharmaraja Selvamuthu

    (Department of Mathematics, Indian Institute of Technology Delhi)

  • Guglielmo D’Amico

    (Department of Pharmacy, University G. d’Annunzio of Chieti-Pescara)

  • Raimondo Manca

    (Department of MEMOTEF, University of Rome, La Sapienza)

Abstract

This article presents a semi-Markov process based approach to optimally select a portfolio consisting of credit risky bonds. The criteria to optimize the credit portfolio is based on l∞-norm risk measure and the proposed optimization model is formulated as a linear programming problem. The input parameters to the optimization model are rate of returns of bonds which are obtained using credit ratings assuming that credit ratings of bonds follow a semi-Markov process. Modeling credit ratings by semi-Markov processes has several advantages over Markov chain models, i.e., it addresses the ageing effect present in the credit rating dynamics. The transition probability matrices generated by semi-Markov process and initial credit ratings are used to generate rate of returns of bonds. The empirical performance of the proposed model is analyzed using the real data. Further, comparison of the proposed approach with the Markov chain approach is performed by obtaining the efficient frontiers for the two models.

Suggested Citation

  • Puneet Pasricha & Dharmaraja Selvamuthu & Guglielmo D’Amico & Raimondo Manca, 2020. "Portfolio optimization of credit risky bonds: a semi-Markov process approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-14, December.
    • Handle: RePEc:spr:fininn:v:6:y:2020:i:1:d:10.1186_s40854-020-00186-1
    DOI: 10.1186/s40854-020-00186-1
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    Cited by:

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    3. P.-C.G. Vassiliou, 2020. "Non-Homogeneous Semi-Markov and Markov Renewal Processes and Change of Measure in Credit Risk," Mathematics, MDPI, vol. 9(1), pages 1-27, December.
    4. Julio Cezar Soares Silva & Adiel Teixeira de Almeida Filho, 2023. "A systematic literature review on solution approaches for the index tracking problem in the last decade," Papers 2306.01660, arXiv.org, revised Jun 2023.

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