The price discovery properties of clean tanker freight futures : unbiasedness, causality and forecasting

Abstract

The scope of this thesis is to examine the price discovery properties of clean tanker freight futures. This is conducted by testing the unbiasedness hypothesis, the lead-lag relationship between freight futures and spot rates and the forecasting properties of freight futures with regards to the underlying spot rates. The research focuses on the most liquid clean tanker freight futures, which are those written on the routes TC2, TC4 and TC5. The results indicate that unbiasedness depends on the route in question and time to maturity. For a one-month horizon of TC2 and one-, two- and three-month horizons of TC5, the unbiasedness hypothesis is found to hold. Unbiasedness is also indicated for the two- and three-month horizons of TC2, but due to weak evidence no conclusions are drawn. For TC4 the unbiasedness hypothesis is rejected. The results from testing the lead-lag relationship indicate that futures prices lead spot rates for all the routes, but the relationship is found to be bi-directional for TC4. When investigating the forecasting performance of end-of-month freight futures it is found that univariate models generally are outperformed by a random walk, indicating that forecasts should not be based on historic spot prices alone. The multivariate models confirm this finding as they generally produce more accurate forecasts than their univariate cousins. Multivariate time-series models were generally found able to outperform forecasts indicated by outright futures prices for one- and two-month horizons, but for a three-month horizon the futures performed as well as or better than the multivariate models. These results imply that the investigated freight futures contain valuable information about future spot rates. Problems regarding the stationarity of the series were experienced throughout the thesis. Because of this it is recommended that the tests performed in this thesis are repeated in a few years when more data is available.