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Estimating the probability of default for shipping high yield bond issues

Author

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  • Grammenos, C.Th.
  • Nomikos, N.K.
  • Papapostolou, N.C.

Abstract

This paper uses a binary logit model to predict the probability of default for high yield bonds issued by shipping companies. Our results suggest that two liquidity ratios, the gearing ratio, the amount raised over total assets ratio, and an industry specific variable are the best estimates for predicting default at the time of issuance. In-and-out-of-sample tests further indicate the predictive ability and robustness of our model. The results are of interest to institutional and individual investors as they can identify which factors to look at when making investment decisions, and which issues have a high likelihood to default; shipowners can also benefit by identifying the factors they need to focus on, in order to offer an issue that does not have a high probability of default.

Suggested Citation

  • Grammenos, C.Th. & Nomikos, N.K. & Papapostolou, N.C., 2008. "Estimating the probability of default for shipping high yield bond issues," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 44(6), pages 1123-1138, November.
    • Handle: RePEc:eee:transe:v:44:y:2008:i:6:p:1123-1138
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    Citations

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    Cited by:

    1. Sunghwa Park & Hyunsok Kim & Janghan Kwon & Taeil Kim, 2021. "Empirics of Korean Shipping Companies’ Default Predictions," Risks, MDPI, vol. 9(9), pages 1-17, September.
    2. Alexandridis, George & Kavussanos, Manolis G. & Kim, Chi Y. & Tsouknidis, Dimitris A. & Visvikis, Ilias D., 2018. "A survey of shipping finance research: Setting the future research agenda," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 115(C), pages 164-212.
    3. Andriosopoulos, Kostas & Doumpos, Michael & Papapostolou, Nikos C. & Pouliasis, Panos K., 2013. "Portfolio optimization and index tracking for the shipping stock and freight markets using evolutionary algorithms," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 52(C), pages 16-34.
    4. Zacharias G. Bragoudakis & Stelios Th. Panagiotou & Helen A. Thanopoulou, 2015. "Greek Shipping Earnings and Investment Expenditure: Exploring the Pre & Post "Ordering -Frenzy" Period," SPOUDAI Journal of Economics and Business, SPOUDAI Journal of Economics and Business, University of Piraeus, vol. 65(3-4), pages 3-28, july-Dece.
    5. Nicoleta BARBUTA-MISU, 2011. "A Specific Model for Assessing the Financial Performance:Case study on Building Sector Enterprises of Galati County - Romania," Risk in Contemporary Economy, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, pages 318-325.
    6. Zacharias G. Bragoudakis & Stelios Panagiotou & Helen Thanopoulou, 2013. "Investment strategy and Greek shipping earnings: exploring the pre & post "ordering-frenzy" period," Working Papers 157, Bank of Greece.
    7. Nicoleta Barbuta-Misu, 2012. "Aggregated Index for Modelling the Influence of Financial Variables on Enterprise Performance," EuroEconomica, Danubius University of Galati, issue 2(31), pages 155-165, May.
    8. Agata Lozinskaia & Andreas Merikas & Anna Merika & Henry Penikas, 2017. "Determinants of the probability of default: the case of the internationally listed shipping corporations," Maritime Policy & Management, Taylor & Francis Journals, vol. 44(7), pages 837-858, October.
    9. Pouliasis, Panos K. & Papapostolou, Nikos C. & Kyriakou, Ioannis & Visvikis, Ilias D., 2018. "Shipping equity risk behavior and portfolio management," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 178-200.
    10. Grammenos, Costas Th. & Papapostolou, Nikos C., 2012. "US shipping initial public offerings: Do prospectus and market information matter?," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(1), pages 276-295.
    11. Mitroussi, K. & Abouarghoub, W. & Haider, J.J. & Pettit, S.J. & Tigka, N., 2016. "Performance drivers of shipping loans: An empirical investigation," International Journal of Production Economics, Elsevier, vol. 171(P3), pages 438-452.
    12. Panayides, Photis M. & Lambertides, Neophytos & Cullinane, Kevin, 2013. "Liquidity risk premium and asset pricing in US water transportation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 52(C), pages 3-15.
    13. Kavussanos, Manolis G. & Tsouknidis, Dimitris A., 2016. "Default risk drivers in shipping bank loans," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 94(C), pages 71-94.
    14. Drobetz, Wolfgang & Haller, Rebekka & Meier, Iwan, 2016. "Cash flow sensitivities during normal and crisis times: Evidence from shipping," Transportation Research Part A: Policy and Practice, Elsevier, vol. 90(C), pages 26-49.
    15. Jane Haider & Zhirong Ou & Stephen Pettit, 2019. "Predicting corporate failure for listed shipping companies," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 21(3), pages 415-438, September.
    16. Mark Clintworth & Dimitrios Lyridis & Evangelos Boulougouris, 2023. "Financial risk assessment in shipping: a holistic machine learning based methodology," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 25(1), pages 90-121, March.
    17. Drobetz, Wolfgang & Gounopoulos, Dimitrios & Merikas, Andreas & Schröder, Henning, 2013. "Capital structure decisions of globally-listed shipping companies," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 52(C), pages 49-76.

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