Commodity risk in European dairy firms

Guillaume Bagnarosa, Mark Cummins, Michael Dowling and Fearghal Kearney

European Review of Agricultural Economics, 2022, vol. 49, issue 1, 151-181

Abstract: We apply a multivariate mixed-data sampling (MIDAS) conditional quantile regression technique to understand the dairy commodity exposure of European dairy firms. Leveraging a theoretically sound hedonic dairy pricing framework, we show that our approach is able to identify both market and operational risk. Profit margins for butter and milk price are particularly important for operational performance. Additional tests are provided, including an application of MIDAS quantile on a period of amplified dairy market risk. Our approach thus allows dairy firms to gain new perspectives on the significant risks posed by the current structure of dairy production in Europe.

Keywords: European dairy firms; dairy commodities; mixed-data sampling; dairy markets (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1093/erae/jbab050 (application/pdf)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oup:erevae:v:49:y:2022:i:1:p:151-181.

Ordering information: This journal article can be ordered from
https://academic.oup.com/journals

Access Statistics for this article

European Review of Agricultural Economics is currently edited by Timothy Richards, Salvatore Di Falco, Céline Nauges and Vincenzina Caputo

More articles in European Review of Agricultural Economics from Oxford University Press and the European Agricultural and Applied Economics Publications Foundation Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, UK. Contact information at EDIRC.
Bibliographic data for series maintained by Oxford University Press ().