Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/237209 
Year of Publication: 
2020
Citation: 
[Journal:] Financial Innovation [ISSN:] 2199-4730 [Volume:] 6 [Issue:] 1 [Publisher:] Springer [Place:] Heidelberg [Year:] 2020 [Pages:] 1-14
Publisher: 
Springer, Heidelberg
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 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.
Subjects: 
Semi-Markov process
Credit ratings
Credit risky bonds
Portfolio optimization
Min-max absolute deviation
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

Files in This Item:
File
Size
418.17 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.