& Price volatility has an important impact on seafood markets and the aquaculture industry. This article investigates price volatility regimes along three dimensions; technology, species and product form. We identify regimes using the Iterated Cumulative Sum of Squares (ICSS), which allow us to compare the volatility found in aquaculture products, as well as comparing against fish-meal and soybeans. The results help identify the level of price risk found within the aquaculture industry across species and products. In addition, we differentiate between technologies comparing wild-caught fish with aquaculture products. The results also help us consider the relative riskiness of aquaculture compared to other industries. The results indicate that in aggregate, price volatility for aquacultured products is substantially lower than for wild. However, this varies substantially between species.

Twenty years ago, Pindyck and Rotemberg concluded that commodity prices exhibited excessive co-movements and that commodity markets were characterized by herd behaviour. The herding hypothesis has recently experienced a revival. A number of studies have concluded that commodities have become 'financialized' and contaminated by the stock market because of the large influx of hedge funds, index trackers and financial investors. Analysing monthly prices of 20 commodities for the period 1986-2010, we find that there has been a tendency toward increased co-movements across commodities and between commodities and the stock market after 2004. However, this result is mainly driven by the extreme price movements after 2008. There is no strong evidence of financialization or contamination from the market activities of financial investors prior to 2008.

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Commodities constitute a nonhomogeneous asset class. Return distributions differ widely across different commodities, both in terms of tail fatness and skewness. These are features that we need to take into account when modeling risk. In this paper, we outline the return characteristics of nineteen different commodity futures during the period 1992-2013. We then evaluate the performance of two standard risk modeling approaches, ie, RiskMetrics and historical simulation, against a quantile regression (QR) approach. Our findings strongly support the conclusion that QR outperforms these standard approaches in predicting value-at-risk for most commodities.

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