Paper 3: Hedging salmon price risk
7 Contributions and limitations
7.2 Empirical contributions
In the following I present this thesis’ empirical contributions relevant for the salmon market participants and/or academic researchers.
First, Bloznelis (2016b) finds increased conditional correlations between prices of different weight classes of salmon in 2007-2013, suggesting that salmon has become more homogenous across the weight classes. This shrinks the potential for diversification using different weight classes (to which there are also biological limits). In turn, this is conducive to the functioning of the futures market for salmon in the following two respects. First, the unmet demand for hedging may shift away from weight-class diversification and plausibly towards hedging using the salmon futures.
Second, salmon futures has become a better hedging instrument for the different weight classes as the price dynamics across the weight classes turned more homogenous and closer to the underlying of the futures contracts.
Second, Bloznelis (2016c) provides an example where forecast combinations do not outperform individual forecasts. This is in contrast to the typical findings and may merit a study on the conditions that yield this phenomenon.
Third, Bloznelis (2016c) finds the spot price to be essentially unpredictable beyond seasonality.
This provides an indication for where the attention could be focused in the future studies of salmon price forecasting. An accurate seasonal forecast may be the key to an accurate price forecast.
Unpredictability beyond seasonality also suggests weak form efficiency in the spot market for salmon; the finding is relevant for the market participants’ pricing strategies and formation of the price expectations.
Fourth, Bloznelis (2016a) demonstrates how optimal hedge ratios could be calculated given the newly proposed objective function of a hedger. The paper discusses the values and variability of the optimal hedge ratios in the recent years for hedging the spot price of salmon with salmon futures.
This might provide a useful starting point for a commercial hedger.
Fifth, Bloznelis (2016a) quantifies the potential uncertainty reduction due to hedging based on the recent data. Relative reduction in the mean squared forecast error is documented for different weight classes and different hedging strategies using salmon futures contracts. Cross hedging with live cattle, soybean meal and oil futures and the share price of Marine Harvest is shown to be inauspicious.
Sixth, the three research papers together provide a broad overview of options of uncertainty management available for the Norwegian farmed Atlantic salmon industry. This should enhance the practitioners’ awareness and understanding of the different strategies and their expected effects.
7.3 Limitations
Due to the nature of the research problems, the thesis does not focus on causal explanations.
While the choice of questions to ask and problems to solve can hardly be classified as a serious limitation of a study, it could have nevertheless been interesting to examine causality of the observed phenomena. Hence, a more structural approach to spot price forecasting and hedging may merit academic studies in the future.
A necessary minimum of assumptions is taken on in the three papers. Most of the models used are linear, which is a common simplification in the economics literature. Bloznelis (2016c) and Bloznelis (2016a) use the rolling-window approach which should lend robustness to the findings and essentially provides a robustness check in itself; for example, Bloznelis (2016a) shows the variation in the optimal hedge ratios over time. Model selection is fully automated in all the three papers, following explicit and concrete principles; hence, the influence of human judgement and the inherent tendency of selective reporting (Ioannidis, 2005) is limited. Modelling procedures being automated
raises hope that the modelling approach could continue yielding similar results as those presented in the research papers, absent structural changes in the data generating processes. There remains a question whether findings stemming from a predictive modelling approach and reduced-form time series models are inherently more or less robust than those from explanatory approach and structural time series models. The key difference is absence or presence of theory-implied assumptions on the models. As long as the assumptions closely reflect the reality, they may be expected to robustify the modelling process and the results. However, once inappropriate assumptions are taken upon, the resultant inflexibility may prevent finding more appropriate models.
There are limitations on the applicability of the findings in practice. The focus on the short-term phenomena makes the results relatively sensitive to ageing. However, this is more or less true in all empirical studies. Crucially, the approaches taken in the papers are morally up to date and will lend themselves directly to repeated applications on any new data to come. On the other hand, it is noteworthy that advanced knowledge of econometrics and statistics supplemented by sufficient experience with statistical software will be needed to replicate the analysis. The latter may be perceived as a natural prerequisite for success in today’s data-rich and technologically capable world.
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