Price Volatility Modelling – Wheat: GARCH Model Application
K. Malec and
AGRIS on-line Papers in Economics and Informatics, 2017, vol. 09, issue 4
This paper is focused on the modelling of volatility in the agricultural commodity market, specifically on wheat. The aim of this study is to develop an applicable and relevant model of conditional heteroscedasticity from the GARCH family for wheat futures prices. The GARCH (1,1) model has the ability to capture the main characteristics of the commodity market, specifically leptokurtic distribution and volatility clustering. The results show that the forecasted volatility of wheat has a tendency towards standard error reversion in the long-run and the position of price distribution is closed to the normal distribution. The wheat production can be hedged against the price variability with long-term contracts. The price of wheat was influenced during the years of 2005 to 2015 by different events, in particular; financial crisis, increasing grain demand and cross-sectional price variability. The results suggest that agricultural producers should focus on short-term structural events the wheat market, rather than long-term variability.
Keywords: Agricultural Finance; Research and Development/Tech Change/Emerging Technologies (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
http://ageconsearch.umn.edu/record/276061/files/35 ... mak-malec-maitah.pdf (application/pdf)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:ags:aolpei:276061
Access Statistics for this article
More articles in AGRIS on-line Papers in Economics and Informatics from Czech University of Life Sciences Prague, Faculty of Economics and Management Contact information at EDIRC.
Bibliographic data for series maintained by AgEcon Search ().