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

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 lÉ-norm risk measure and the proposed optimization model is formulated as a linear programming problem. The input par...

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Bibliographic Details
Published in:Financial innovation (Heidelberg) Vol. 6; no. 1; pp. 1 - 14
Main Authors: Pasricha, Puneet, Selvamuthu, Dharmaraja, D'Amico, Guglielmo, Manca, Raimondo
Format: Journal Article
Language:English
Published: Heidelberg Springer 22.05.2020
Springer Berlin Heidelberg
Springer Nature B.V
SpringerOpen
Subjects:
Credit ratings
Credit risky bonds
Economics
Economics and Finance
Linear programming
Macroeconomics/Monetary Economics//Financial Economics
Markov analysis
Min-max absolute deviation
Optimization
Political Economy/Economic Systems
Portfolio optimization
Semi-Markov process
Portfolio optimization
Semi-Markov process
Credit risky bonds
Min-max absolute deviation
Credit ratings
ISSN:2199-4730, 2199-4730
Online Access:Get full text
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Summary: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 lÉ-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.
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ISSN:2199-4730
2199-4730
DOI:10.1186/s40854-020-00186-1