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.

中文翻译：

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信用风险债券的投资组合优化：一种半马尔可夫过程方法

本文提出了一种基于半马尔可夫过程的方法来优化选择由信用风险债券组成的投资组合。优化信贷组合的标准基于 l ∞ -范数风险度量，并且所提出的优化模型被表述为线性规划问题。优化模型的输入参数是债券收益率，假设债券的信用评级遵循半马尔可夫过程，则使用信用评级获得。通过半马尔可夫过程建模信用评级比马尔可夫链模型有几个优点，即它解决了信用评级动态中存在的老化效应。由半马尔可夫过程和初始信用评级生成的转移概率矩阵用于生成债券的收益率。使用真实数据分析了所提出模型的经验性能。此外，通过获得两个模型的有效边界来执行所提出的方法与马尔可夫链方法的比较。