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Value-at-risk analysis of the asymmetric long-memory volatility process of dry bulk freight rates

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  • Chao-Chi Chang

    (Department of Applied Foreign Languages, Lang-Yang Institute of Technology, Taiwan.)

  • Heng Chih Chou

    (Department of Shipping and Transportation Management, National Taiwan Ocean University, Taiwan.)

  • Chun Chou Wu

    (National Kaohsiung First University of Technology, Taiwan.)

Abstract

This study aims to apply value-at-risk (VaR) models to evaluate the risk of dry bulk freight rates when there is an asymmetric long-memory volatility process. The VaR estimations as well as expected shortfalls for both short and long trading positions are conducted. We use the Fractionally Integrated GARCH, Hyperbolic GARCH and Fractionally Integrated APARCH models to analyse the performance of the VaR models with the normal, Student-t and skewed Student-t distributions. Empirical results suggest that precise VaR estimates may be obtained from an asymmetric long-memory volatility structure with the skewed Student-t distribution. Moreover, the asymmetric FIAPARCH model outperforms than other models in out-of-sampling forecasting. Therefore, our findings provide a more accurate estimation of VaR for dry bulk freight rates. These results present several potential implications for dry bulk freight market risk quantification and hedging strategies.

Suggested Citation

  • Chao-Chi Chang & Heng Chih Chou & Chun Chou Wu, 2014. "Value-at-risk analysis of the asymmetric long-memory volatility process of dry bulk freight rates," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 16(3), pages 298-320, September.
    • Handle: RePEc:pal:marecl:v:16:y:2014:i:3:p:298-320
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    Cited by:

    1. Liu, Junlin & Chen, Feier, 2018. "Asymmetric volatility varies in different dry bulk freight rate markets under structure breaks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 316-327.
    2. Zouheir Mighri & Raouf Jaziri, 2023. "Long-Memory, Asymmetry and Fat-Tailed GARCH Models in Value-at-Risk Estimation: Empirical Evidence from the Global Real Estate Markets," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 21(1), pages 41-97, March.
    3. Martial Phélippé-Guinvarc'H & Jean Cordier, 2015. "Machine Learning for Semi-Strong Efficiency Test of Inter-Market Wheat Futures," Post-Print hal-02151848, HAL.

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