Fish Price Volatility

Atle OglendRoy Endré Dahl

FAO FPI Workshop, Ischia, 03.10.13

Introduction

• Fishing is a risky venture– Price variability is a significant risk component

• Few studies on fish-price volatility, relative to other agricultural commodities– Data availability– Single species volatility studies (e.g. Oglend and

Sikveland, 2008; Oglend, 2013, Sollibakken, 2012; Buguk et. al. 2003)

– Forecast studies (e.g. Guttormsen, 1999; Vukina and Anderson, 1994; Gu and Anderson, 1995).

Introduction

• We utilize trade-data to analyse fish-price volatility on a global scale– Fish is a highly traded commodity group

• Accounts for 10 percent of total agricultural exports (FAO, 2012)

– Data allows statistical analysis of volatility

• 2012 State of World Fishers report (FAO):“In the next decade, with aquaculture accounting for a

much larger share of total fish supply, the price swings of aquaculture products could have a significant impact on price formation in the sector overall, possibly leading to more volatility”

Volatility in Agricultural Prices

• Price volatility suggests a fundamental level of uncertainty and risk– Generally considered undesriable– Not all price variations imply uncertainty

• Terms of trade variability can have a negative impact on economic growth

• Variability in agricultural prices are generally considered high– Varies over time and across products

Volatility in Agricultural Prices

• Supply shocks generally considered more important than demand shocks in terms of price movements– Variations in catches, stocks, quotas, growth conditions,

climate, diseases etc.

• Elasticity of demand and short-run supply is low– Higher el. of supply for aq. than capture fisheries

• Elasticity of income is low => macro-economic fluctuations are of less direct importance to prices– Impact through input factor prices

• Storability reduces volatility – Higher volatility for fresh fish

• National trade policies can influence global prices

The FAO fish-price index

• The FAO fish price index summarizes common trends in fish prices world-wide

10

01

50

20

0

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010

Food Price IndexMeat Price IndexFish Price Index

Data

• Monthly trade-data on fish and fishery products imported to the U.S., Japan and EU – Important import markets– Reliability of data

• Data provided by the Norwegian Seafood Council in cooperation with the FAO

• The data is characterized along four dimensions: – Markets: EU, Japan, U.S.– Technology: Aquaculture, Capture Fisheries– Species groups: White Fish, Salmonidae, Crustaceans,

Tuna, Pelagic excluding Tuna, Other Fish, – Product forms: fresh, frozen, filet, whole and fishmeat

• Observations from January 1990 to October 2012

Data

• Price = unit import-value• Volatility = std.dev. of month-by-month log-returns

– Avoids potential trend effects

• 106 unique prices – Some missing values in the data – We exclude prices with less than 30 observations

• Additional variables considered when comparing volatilities:– # Obs. used to calculate volatility– Sampling heterogeneity– Monthly average trade volume– C.V. of trade volume

Volatility for different groups

Method

Method

2. Regime Analysis– Iterated Cumulative Sum of Squares (Inclan and

Tiao, 1994) • Originally developed to identify volatility regimes in

financial time-series

– We look for differences in volatility across time for each category

– Only applied to prices with complete observations– Volatilities in each category is weighted by trade

volume

Accounts for statistical variations across time

Conditioning Variables and Volatility

           Regression of Log Std. Dev. on different conditioning variables.             Variable Coefficient t-stat Specification Tests (p-values)  Constant -0.213 -0.435 AR 1-2 test: 0.547  Log Mean Trade Volume -0.170 -6.670 ARCH 1-1 test: 0.165  Log # obs. -0.023 -0.185 Normality test: 0.334  Log C.V. Trade Volume 0.548 6.180 Hetero test: 0.459  Log sampling Heterogeneity 0.023 0.065 Hetero-X test: 0.722        RESET23 test: 0.087                    

Conditioning Variables and Volatility

Volatility Across Markets

1. Regression Analysis Results– No statistically significant differences in volatility across

markets

2. Regime Analysis Results

Volatility Across Technology

1. Regression Analysis Results– Aquaculture prices significantly lower volatility than

capture fisheries(wild) prices

2. Regime Analysis Results

Volatility Across Species

1. Regression Analysis Results– Significantly higher volatility for the pelagic group

• Supply from capture fisheries• Robust against trade volume and trade variations

– Some evidence that the Salmonidae group has lower volatility

• Significant only when trade variation is accounted for

Volatility Across Species

2. Regime Analysis Results

Volatility Across Product Forms

1. Regression Analysis Results– Fresh vs. Frozen

• No evidence for significant differences in volatility

– Filet, Fish-meat and Whole fish• Filet has significantly lower volatility than whole fish

and fish-meat• Robust across trade quantity and variation

Volatility Across Product Forms

2. Regime Analysis Results

Volatility Across Product Forms

2. Regime Analysis Results

Volatility rankings (lower is higher volatility)

Volatility rankings (lower is higher volatility)

To Summarize…

• Differences in volatility across fish categories– No significant difference in volatility across import

markets– Aquaculture lower volatility than capture fisheries– Pelagic highest volatility of the species group– No significant differences between fresh and frozen fish– Filets lower volatility than whole fish and fish-meat

• Fish vs. other commodities– Aquaculture comparable volatility to other commodities,

but above chicken and feeder-cattle– Wild fish (pelagic) very high volatility