A portfolio optimization model for corporate bonds subject to credit risk

Akutsu, Nagisa; Kijima, Masaaki; Komoribayashi, Katsuya

doi:10.21314/jor.2004.090

Cited by 10 publications

(8 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We apply the proposed change of measure to the historical kernel estimated from the data and obtain risk neutral transition probabilities. For the illustration purpose we have considered current yield for all the credit risky bonds and the risk free government bond from Akutsu et al (2003) and risk premium adjustments from Kijima and Komoribayashi (1998). Average returns for each period is obtained taking the average of the returns simulated for the two sectors over all the scenarios.…”

Section: Empirical Analysismentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

Section: Empirical Analysismentioning

confidence: 99%

“…Models for credit quality based on ratings are also frequently used in the pricing and risk management literature, see for example Jarrow et al (1997); Kijima and Komoribayashi (1998); Akutsu et al (2003). In 1997, Jarrow et al (1997 applied for the first time Markov processes to capture the time evolution of credit ratings.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The formulation of the loan portfolio problem we propose in this paper harnesses the historical loan performance data commonly available and facilitates the treatment of the large problems that are common in practice. Andersson, Mausser, Rosen & Uryasev (2001), Akutsu, Kijima & Komoribayashi (2004), Kraft & Steffensen (2008), Meindl & Primbs (2006), Wise & Bhansali (2002), and others study static and dynamic corporate bond portfolio selection problems using expected utility or other objectives. Capponi & Figueroa-Lopez (2014) and Capponi, Figueroa-Lopez & Pascucci (2015) develop regime-switching models to address the dynamic asset allocation problem in defaultable markets.…”

Section: Related Literaturementioning

confidence: 99%

Large-Scale Loan Portfolio Selection

2015

View full text Add to dashboard Cite

We consider the problem of optimally selecting a large portfolio of risky loans, such as mortgages, credit cards, auto loans, student loans, or business loans. Examples include loan portfolios held by financial institutions and fixed-income investors as well as pools of loans backing mortgage-and asset-backed securities. The size of these portfolios can range from the thousands to even hundreds of thousands. Optimal portfolio selection requires the solution of a high-dimensional nonlinear integer program and is extremely computationally challenging. For larger portfolios, this optimization problem is intractable. We propose an approximate optimization approach that yields an asymptotically optimal portfolio for a broad class of data-driven models of loan delinquency and prepayment. We prove that the asymptotically optimal portfolio converges to the optimal portfolio as the portfolio size grows large. Numerical case studies using actual loan data demonstrate its computational efficiency. The asymptotically optimal portfolio's computational cost does not increase with the size of the portfolio. It is typically many orders of magnitude faster than nonlinear integer program solvers while also being highly accurate even for moderate-sized portfolios. AbstractWe consider the problem of optimally selecting a large portfolio of risky loans, such as mortgages, credit cards, auto loans, student loans, or business loans. Examples include loan portfolios held by financial institutions and fixed-income investors as well as pools of loans backing mortgage-and asset-backed securities. The size of these portfolios can range from the thousands to even hundreds of thousands. Optimal portfolio selection requires the solution of a high-dimensional nonlinear integer program and is extremely computationally challenging. For larger portfolios, this optimization problem is intractable. We propose an approximate optimization approach that yields an asymptotically optimal portfolio for a broad class of data-driven models of loan delinquency and prepayment. We prove that the asymptotically optimal portfolio converges to the optimal portfolio as the portfolio size grows large. Numerical case studies using actual loan data demonstrate its computational efficiency. The asymptotically optimal portfolio's computational cost does not increase with the size of the portfolio. It is typically many orders of magnitude faster than nonlinear integer program solvers while also being highly accurate even for moderate-sized portfolios.

show abstract

“…In the aftermath of the economic shock during the turbulent 1970s, research began to refine the portfolio concept and introduced measures on how to account for business and economic risks (Reilly & Joehnk, 1976). It then advanced to simulating the impact of rating transitions into the choice of bonds in compliance with the investor's readiness to assume risk (see, e.g., Akutsu, Kijima, & Komoribayashi, 2003; Fabozzi & Tunaru, 2007; Lucas et al, 2001; Schäftner, 2008). Especially in recent years, the incorporation of such events into the optimization of bond portfolios gained much attention.…”

Section: Five Themes Most Critical To Key Stakeholders Of Creditmentioning

confidence: 99%

Rating Agencies as Gatekeepers to the Capital Market: Practical Implications of 40 Years of Research

Lagner¹,

Knyphausen‐Aufseß²

2012

Financial Market

View full text Add to dashboard Cite

This paper gives an extensive overview on the avenues academic research has taken in exploring the role of credit ratings. In doing so, it relies on a strict methodological approach for the collection and evaluation of relevant studies to address the criticisms on the lack of profoundness in prior literature reviews in business research. It provides therefore to both academics and practitioners an exhaustive compendium on the role and relevance of credit ratings for each key stakeholder over the past 40 years. Second, it pinpoints specifically to how credit ratings impact corporate strategy. In this matter, it suggests to focus on this underresearched aspect in future as it has become a critical factor in the decision‐making of company boards.

show abstract

A portfolio optimization model for corporate bonds subject to credit risk

Cited by 10 publications

References 0 publications

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

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

Large-Scale Loan Portfolio Selection

Rating Agencies as Gatekeepers to the Capital Market: Practical Implications of 40 Years of Research

Contact Info

Product

Resources

About