Price Transmission in Indian Shrimp Exports Public Deposited

http://ir.library.oregonstate.edu/concern/conference_proceedings_or_journals/vh53wx68j

Proceedings of the Eighteenth Biennial Conference of the International Institute of Fisheries Economics and Trade, held July 11-15, 2016 at Aberdeen Exhibition and Conference Center (AECC), Aberdeen, Scotland, UK.

Suggested Bibliographic Reference: Challenging New Frontiers in the Global Seafood Sector: Proceedings of the Eighteenth Biennial Conference of the International Institute of Fisheries Economics and Trade, July 11-15, 2016. Compiled by Ann L. Shriver and Stefani J. Evers. International Institute of Fisheries Economics and Trade (IIFET), Corvallis, 2016.

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  • This study was conducted with the aim of modeling and forecasting the Indian shrimp export prices to the USA. Two major species mainly Black tiger (Penaeus monodon), white legged shrimp (Litopenaeus Vannmei) headless, shell on form, grade 16/20 were considered for the study. Published data on weekly price indicators of marine products exports (PRIME) of Marine products Exports Development Authority of India (MPEDA) for 204 weeks were collected. Multiple models were used for modeling and forecasting the price series. The results of Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) model gave the best results. The other models used in this analysis and which gave higher RMSE values where ARIMA, ANN, GARCH and EGARCH. The empirical results have supported the theory that the EGARCH model can capture asymmetric volatility. Value at risk (VAR) is a widely used measure of risk and measures the maximum loss that can be incurred. However, GARCH provides a methodology to calculate the future values of standard deviation from which the VAR is derived. Thus, suitable GARCH model can produce forecasted standard deviation and also the future values of the VAR. In order to validate the predicted standard deviation from a GARCH model these predicted values were compared with the standard deviation calculated from out of sample data which became available subsequently. The results of the calculated standard deviation and the standard deviation estimated by the VAR indicated similar result. Hence GARCH can be used to forecast volatility effectively.
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  • description.provenance : Submitted by IIFET Student Assistant (iifetstudentassistant@gmail.com) on 2016-11-22T21:17:42Z No. of bitstreams: 2 Qureshi0040.pdf: 769959 bytes, checksum: 00552f9217ecc38451aa41adb16981ac (MD5) Qureshi40ppt.pdf: 755923 bytes, checksum: a53a82187b0cbc8a6d1b0cce392f7bd3 (MD5)
  • description.provenance : Approved for entry into archive by Michael Boock(michael.boock@oregonstate.edu) on 2016-11-22T23:56:30Z (GMT) No. of bitstreams: 2 Qureshi0040.pdf: 769959 bytes, checksum: 00552f9217ecc38451aa41adb16981ac (MD5) Qureshi40ppt.pdf: 755923 bytes, checksum: a53a82187b0cbc8a6d1b0cce392f7bd3 (MD5)
  • description.provenance : Made available in DSpace on 2016-11-22T23:56:30Z (GMT). No. of bitstreams: 2 Qureshi0040.pdf: 769959 bytes, checksum: 00552f9217ecc38451aa41adb16981ac (MD5) Qureshi40ppt.pdf: 755923 bytes, checksum: a53a82187b0cbc8a6d1b0cce392f7bd3 (MD5)
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  • 0976343290

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