Market risk model selection and medium-term risk with limited data: Application to ocean tanker freight markets

The estimation of medium-term market risk dictated by limited data availability, is a challenging issue of concern amongst academics and practitioners. This paper addresses the issue by exploiting the concepts of volatility and quantile scaling in order to determine the best method for extrapolating medium-term risk forecasts from high frequency data. Additionally, market risk model selection is investigated for a new dataset on ocean tanker freight rates, which refer to the income of the capital good - tanker vessels. Certain idiosyncrasies inherent in the very competitive shipping freight rate markets, such as excessive volatility, cyclicality of returns and the medium-term investment horizons - found in few other markets - make these issues challenging. Findings indicate that medium-term risk exposures can be estimated accurately by using an empirical scaling law which outperforms the conventional scaling laws of the square and tail index root of time. Regarding the market risk model selection for short-term investment horizons, findings contradict most studies on conventional financial assets: interestingly, freight rate market risk quantification favors simpler specifications, such as the GARCH and the historical simulation models.