In this master thesis, I study the futures offered by Fish Pool ASA to analyze their efficiency as a risk management tool in the Atlantic salmon market. The dataset used stretches over 9 year's form 2006 to 2015. I use both weekly and biweekly data and the dataset is divided into three samples; full-sample, in-sample and out-of-sample. I apply four different models to find the optimal hedge ratio. These models are the naïve hedge, OLS-regression, VAR and VECM. I find that the OLS-regression performs the best, as it yields the highest variance reduction in all samples. However, the differences between the different models are marginal. Somewhat unexpected we observe that the naïve hedge performs on level with the other models. It even exceed the variance reduction of both the VAR- and VEC-model in two of the samples. Our findings in this thesis suggest that simple models like the naïv hedge and OLS-regression might lead to superior hedging strategies compared to those of the VAR- and VEC-model. Compared with other futures with agricultural commodities as underlying asset, we observe that hedging efficiency achieved by the futures offered by Fish Pool is in the mid-range of what other studies observe.
|Educations||MSc in Finance and Accounting, (Graduate Programme) Final Thesis|
|Number of pages||83|