A b s t r a c t 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.

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This study investigates the risk-return relations in dry-bulk shipping freight, and to analyse how it was influenced by the 2008 financial tsunami. Empirical results show that the shipping freight's risk-return relation, measured by risk premium parameter β, varies by different types of ship. The risk-return relations of capesize freight have changed after the financial tsunami, from high-risk/high-return into high-risk/low-return. In other words, compared to the case of Standard & Poor's 500 (S&P 500), there have been significant declines in the freight risk premiums. Furthermore, the risk premium parameter β is not only affected by the financial tsunami, but also significantly affected by the previous parameter β and previous freight return. The results of this study can make shipping operators aware of the dynamics of risk-return relations among various ships, so as to secure the optimal asset allocation of ship investments.