This study aims to investigate the features of tanker freight rates when there is a long memory effect. We employed the GPH test, the GSP test, the HYGARCH and the FIEGARCH models for the long memory test and estimation. Our results suggest that precise estimates of tanker freight rates may be acquired from a long memory in volatility models with the skewed Student-t distribution. Such models improve the long-term volatility forecast and produce more precise pricing of tanker freight contracts. Moreover, for the appropriate risk evaluation of tanker freight rates, the degree of persistence should be examined and modelling that includes volatility clustering, asymmetry, leptokurtosis and long range dependence should be considered. Therefore, we could extend these findings to risk management in the tanker freight markets.