Tanker shipping is the primary means for the transportation of petroleum and petroleum products around the world and thus plays a crucial role in the energy supply chain. However, the high volatility of tanker freight rates has been a major concern for market participants and led to the development of the tanker freight derivatives in the form of forward freight agreements (FFAs). The aim of this paper is to investigate the performance of these instruments in managing tanker freight rate risk. Using a data set for six major tanker routes covering the period between 2005 and 2013, we examine the effectiveness of alternative hedging methods, including a bivariate Markov Regime Switching GARCH model, in hedging tanker freight rates. The regime switching GARCH specification links the concept of equilibrium freight rate determination underlying different market conditions and the dynamics of the conditional second moments across high and low volatility regimes. Overall, we find evidence supporting the argument that the tanker freight market is characterized by different regimes. However, while the use of a regime switching model allows for a significant improvement in the performance of the hedge in-sample, out-of-sample results are mixed.
This paper combines the Heterogeneous Autoregressive Realised Volatility (HAR-RV) model and the Markov Regime Switching (MRS) approach to estimate and forecast volatility of energy futures contracts traded at the Tokyo Commodity Exchange (TOCOM). The proposed MRS-HAR-RV model allows the dynamics of the realised volatility to change as market conditions change. The dataset consists of intraday prices for gasoline, kerosene and crude oil futures. Estimation results suggest MRS-HAR-RV model can capture dynamics of price volatility of energy futures better than alternative models. However, out-of-sample forecast evaluation results show that MRS-HAR-RV can only produce better forecasts for more liquid contracts. Moreover, MRS-HAR-RV model seems to less over-predict and more under-predict the volatility compared to HAR-RV, HAR-RV-CJ, GARCH, and MRS-GARCH models.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.