Journal of the Korean Society of Fisheries and Ocean Technology (수산해양기술연구)

The Korean Society of Fisheries and Ocean Technology (한국수산해양기술학회)
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An analysis of the causality between international oil price and skipjack tuna price

국제 유가 변동과 원양선망어업 가다랑어 가격 간의 인과성 분석

  • JO, Heon-Ju (Distant Water Fisheries Resources Division, National Institute of Fisheries Science) ;
  • KIM, Do-Hoon (Department of Marine & Fisheries Business and Economics, Pukyong National University) ;
  • KIM, Doo-Nam (Distant Water Fisheries Resources Division, National Institute of Fisheries Science) ;
  • LEE, Sung-Il (Distant Water Fisheries Resources Division, National Institute of Fisheries Science) ;
  • LEE, Mi-Kyung (Distant Water Fisheries Resources Division, National Institute of Fisheries Science)
  • 조헌주 (국립수산과학원 원양자원과) ;
  • 김도훈 (부경대학교 해양수산경영학과) ;
  • 김두남 (국립수산과학원 원양자원과) ;
  • 이성일 (국립수산과학원 원양자원과) ;
  • 이미경 (국립수산과학원 원양자원과)
  • Received : 2019.06.20
  • Accepted : 2019.07.05
  • Published : 2019.08.31
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Abstract

The aim of this study is to analyze the relationship between international oil price as a fuel cost in overseas fisheries and skipjack tuna price as a part of main products in overseas fisheries using monthly time series data from 2008 to 2017. The study also tried to analyze the change of fishing profits by fuel cost. For a time series analysis, this study conducted both the unit-root test for stability of data and the Johansen cointegration test for long-term equilibrium relations among variables. In addition, it used not only the Granger causality test to examine interactions among variables, but also the Vector Auto Regressive (VAR) model to estimate statistical impacts among variables used in the model. Results of this study are as follows. First, each data on variables was not found to be stationary from the ADF unit-root test and long-term equilibrium relations among variables were not found from a Johansen cointegration test. Second, the Granger causality test showed that the international oil prices would directly cause changes in skipjack tuna prices. Third, the VAR model indicated that the posterior t-2 period change of international oil price would have an statistically significant effect on changes of skipjack tuna prices. Finally, fishing profits from skipjack would be decreased by 0.06% if the fuel cost increases by 1%.

Keywords

References

  1. Cha YG and Kim KS. 2009. A causality analysis of the prices between imported fisheries and domestic fisheries in distribution channel. J Fish Bus Adm 40, 105-126.
  2. Dickey DA and Fuller WA. 1979. Distribution of the estimators for autoregressive time series with a unit root. ASA, 74, 427-431.
  3. Eom KH, Kim HS, Han IS and Kim DH. 2015. An analysis of changes in catch amount of offshore and coastal fisheries by climate change in Korea. J Fish Bus Adm 46, 31-41. (DOI:10.12939/FBA.2015.46.2.031).
  4. Fisheries Information Service. 2018. Distatnt water fisheries statistic (2013-2017). Ministry of oceans and fisheries. Retrieved from https://www.fips.go.kr/p/S020404/. Accessed 2 Feb 2019.
  5. Gujarati D and Porter D. 2009. Basic econometrics, 5th Edition. McGraw-Hill Korea, Seoul, Korea, 875-915.
  6. Johansen S. 1988. Statistical analysis of cointegration vectors. Journal of economics dynamics and control 12, 231-254. (DOI:10.1016/0165-1889(88)90041-3).
  7. Johansen S. 1991. Estimation and hypothesis testing of cointegration vectors in Gaussian autoregressive models. Econometrica 59, 1551-1580. (DOI:10.2307/2938278).
  8. Kim CH and Nam JO. 2015. A causality analysis of the hairtail price by distribution channel using a vector autoregressive model. J Fish Bus Adm 46, 093-107. (DOI:10.12939/FBA.2015.46.1.093).
  9. Kim DH and Kim HS. 2015. Analyzing price interactions between wild caught fish and farmed fish on the korean seafood market. Journal of fisheries and marine sciences education 26, 1610-1618. (DOI:10.13000/JFMSE.2015.27.6.1610).
  10. Kim SS, Lee MS and Choe YC. 2003. Returns to investment on research in korean agriculture. Korean J. of Ag. Extension 10, 57-76.
  11. Lee SY. 2007. A study on regional pricing structure of fishery market at the consumers sector in Korea. Doctoral Dissertation, Pukyung National University, Busan, South Korea, 143.
  12. Nam JO, Noh SK and Park EY. 2012. A study on forecasting oyster price with time series analysis. Korea Marinetime Institute 27, 66-91.
  13. Overseas Fisheries Information System (OFIS). 2018. Statistical survey of korea distant water fisheries (2013-2018), Ministry of oceans and fisheries. Retrieved from https://www.ofis.or.kr/rb/c/Information/stats. Accessed 2 Feb 2019.
  14. Seo YI, Chung YH and Kim DH. 2014. An analysis on the relationship between prices and catch amounts of sandfish using a cointegration test. J Korean Soc Fish Technol 50, 502-510. (DOI:10.3796/KSFT.2014.50.4.502).
  15. Song IH and Jung WS. 2002. Econometris analysis using SAS and Eviews, Samyungsa, Seoul, Korea, 349-350.
  16. Korea Overseas Fisheries Association (KOFA). 2018. Statistical year book of overseas fisheries. Samchang printing office, Seoul, Korea, 352.