5 research outputs found
The impact of conditional higher moments on risk management: The case of the tanker freight market
Tanker shipping provides the primary means of transportation for almost types of petroleum product traded globally. It is therefore essential to the energy supply chain to be able to correctly evaluate the structure and risk associated with freight rates in this market. This paper examines the concepts of conditional skewness and kurtosis in tanker freight rate. This is because, although the departure from normality of asset returns has been well documented, the relatively recent introduction of the concepts of conditional skewness and conditional kurtosis into the financial market literature, together with the unique shape of the supply curve in shipping markets, means that this has not been fully examined in the shipping literature. This is crucial given that a failure to take these structural characteristics into account could lead to market participants underestimating the probability of extreme and unfavourable events and therefore the consequent risk associated with market operations. Examining a sample of three types of tanker freight rate returns, we find that tanker freight rate returns exhibit conditional higher moments and that models that incorporate conditional skewness and kurtosis provide a more accurate value-at-risk measure and therefore a more accurate measure of the true risk faced by market participants
Risk management, price discovery and forecasting in the freight futures market
Available from British Library Document Supply Centre-DSC:DXN030867 / BLDSC - British Library Document Supply CentreSIGLEGBUnited Kingdo
Estimating the probability of default for shipping high yield bond issues
This paper uses a binary logit model to predict the probability of default for high yield bonds issued by shipping companies. Our results suggest that two liquidity ratios, the gearing ratio, the amount raised over total assets ratio, and an industry specific variable are the best estimates for predicting default at the time of issuance. In-and-out-of-sample tests further indicate the predictive ability and robustness of our model. The results are of interest to institutional and individual investors as they can identify which factors to look at when making investment decisions, and which issues have a high likelihood to default; shipowners can also benefit by identifying the factors they need to focus on, in order to offer an issue that does not have a high probability of default.High yield bonds Probability of default Logit model Shipping