VOLATILITY DYNAMICS IN AQUACULTURE FISH MARKETS
Global trade in seafood has more than doubled from 72 billion USD in 2004 to 148 billion USD in 2014 according to Food and Agricultural Organization (FAO) (2016). Aquaculture is the main contributor to this increase in trade, because of high growth in aquaculture supply (Asche, 2008). While wild landings stagnates due to biological limitations, aquaculture production has increased for several species thanks to improvements in technology and logistics. In a recent paper, Anderson et al. (2017) show increasing commoditizing for the main seafood species groups due to increasing scale and trade globally. Their results agree with Tveteras et al. (2012) whom find well-integrated global markets for most groups of species.
In this paper, we assess volatility dynamics in aquaculture markets. Several papers (Dahl and Oglend, 2014; Asche et al., 2015; Dahl, 2017) argue that the advantages in aquaculture production over wild capture provides a stable supply reducing price volatility. Table 1 provides an overview of the species and markets considered in our study. We apply monthly trade data from 01.1990 to 12.2016 and aggregate a value-weighted price index per region and per species. Moreover, we estimate the cross-sectional volatility (Garcia et al., 2010; Goltz et al., 2011) using each product's dispersion from the index mean. This provide an instantaneous estimate with no need to evaluate other parameters.
Previous studies on volatility dynamics in seafood markets, consider volatility spillover. In particular, Dahl and Jonsson (2017a) examine volatility spillover between seafood markets in EU, Japan and US, and find that events like El Niño/La Niña cause peaks in volatility spillover between EU, Japan and US. In a related article, Dahl and Jonsson (2017b) study volatility spillover between aquaculture and wild, and find that wild products typically transmits volatility to aquaculture products. Moreover, they show that it requires a substantial (negative) supply shock to aquaculture production in order to shift volatility spillover from aquaculture to wild. Both articles show time-varying volatility dynamics.
We apply a time-varying copula to study the volatility dynamics between the aquaculture species. The method provides us with information on relationship over time and between the regions and species considered. The article contributes to previous research by adding knowledge about aquaculture fish markets dynamics. Our results corroborates well with previous research and show considerable time-varying dynamics in price volatility.