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

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.