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

作     者：Puneet Pasricha Dharmaraja Selvamuthu Guglielmo D’Amico Raimondo Manca 

作者机构：Department of MathematicsIndian Institute of Technology DelhiNew Delhi 110016India Department of PharmacyUniversity G.d’Annunzio of Chieti-Pescara66100 ChietiItaly Department of MEMOTEFUniversity of RomeLa Sapienza00161 RomeItaly 

出 版 物：《Financial Innovation》 (金融创新（英文）)

年 卷 期：2020年第6卷第1期

页      面：456-469页

核心收录：

学科分类：12[管理学] 0202[经济学-应用经济学] 1202[管理学-工商管理] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 070105[理学-运筹学与控制论] 0701[理学-数学] 

主　　题：Semi-Markov process Credit ratings Credit risky bonds Portfolio optimization Min-max absolute deviation 

摘      要：This article presents a semi-Markov process based approach to optimally select a portfolio consisting of credit risky *** criteria to optimize the credit portfolio is based on l_(∞)-norm risk measure and the proposed optimization model is formulated as a linear programming *** 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 *** 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 *** transition probability matrices generated by semi-Markov process and initial credit ratings are used to generate rate of returns of *** empirical performance of the proposed model is analyzed using the real ***,comparison of the proposed approach with the Markov chain approach is performed by obtaining the efficient frontiers for the two models.