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The empirical evidence of the leverage effect on volatility in international bulk shipping market

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  • Yung-Shun Chen
  • Shiu-Tung Wang

Abstract

This paper mainly applies Nelson's EGARCH (Exponentially Generalized Autoregressive Conditional Heteroskedasticity) model to investigate the leverage effect in the presence of the international bulk shipping market. The daily return of three different types of bulk vessel in the sampling period selected has been examined. We find that all return series show a significantly negative relation in terms of return and volatility and the leverage effect on volatility is more significant in market downward movement than in market upward movement under the same magnitude of innovation, in addition, the larger vessels have much more leverage effect than smaller vessels contemporaneously. Therefore, it seems to be an inherent nature in the international bulk shipping market that the phenomenon of an asymmetric impact between past innovations and current volatility. This result from the investigation may provide investors with an insight into real characteristics of price return volatility, it is useful for investors to pre-arrange their portfolios of assets, risk management e.g. enabling them to achieve a reduction of investment risk and an increase of operation performance in profit gain.

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  • Yung-Shun Chen & Shiu-Tung Wang, 2004. "The empirical evidence of the leverage effect on volatility in international bulk shipping market," Maritime Policy & Management, Taylor & Francis Journals, vol. 31(2), pages 109-124, April.
    • Handle: RePEc:taf:marpmg:v:31:y:2004:i:2:p:109-124
    DOI: 10.1080/0308883042000208301
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    Cited by:

    1. Lim, Kian Guan & Nomikos, Nikos K. & Yap, Nelson, 2019. "Understanding the fundamentals of freight markets volatility," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 130(C), pages 1-15.
    2. Sun, Xiaolei & Liu, Chang & Wang, Jun & Li, Jianping, 2020. "Assessing the extreme risk spillovers of international commodities on maritime markets: A GARCH-Copula-CoVaR approach," International Review of Financial Analysis, Elsevier, vol. 68(C).
    3. Wenming Shi & Kevin X. Li & Zhongzhi Yang & Ganggang Wang, 2017. "Time-varying copula models in the shipping derivatives market," Empirical Economics, Springer, vol. 53(3), pages 1039-1058, November.
    4. Cullinane, Kevin, 2004. "1. Editorial: Key Themes In Shipping Economics Research," Research in Transportation Economics, Elsevier, vol. 12(1), pages 1-17, January.
    5. Wolfgang Drobetz & Tim Richter & Martin Wambach, 2012. "Dynamics of time-varying volatility in the dry bulk and tanker freight markets," Applied Financial Economics, Taylor & Francis Journals, vol. 22(16), pages 1367-1384, August.
    6. Gavriilidis, Konstantinos & Kambouroudis, Dimos S. & Tsakou, Katerina & Tsouknidis, Dimitris A., 2018. "Volatility forecasting across tanker freight rates: The role of oil price shocks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 118(C), pages 376-391.
    7. Maitra, Debasish & Chandra, Saurabh & Dash, Saumya Ranjan, 2020. "Liner shipping industry and oil price volatility: Dynamic connectedness and portfolio diversification," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 138(C).
    8. Syriopoulos, Theodore C., 2007. "Chapter 6 Financing Greek Shipping: Modern Instruments, Methods and Markets," Research in Transportation Economics, Elsevier, vol. 21(1), pages 171-219, January.
    9. Pelagidis, Theodore & Panagiotopoulos, George, 2019. "Forward Freight Agreements and Market Transparency in the Capesizs Sector," MPRA Paper 107035, University Library of Munich, Germany.

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