Relationship of iron ore price with other major commodity prices

Yoochan Kim (), Apurna Ghosh (), Erkan Topal () and Ping Chang ()
Additional contact information
Yoochan Kim: Western Australian School of Mines, Curtin University
Apurna Ghosh: Western Australian School of Mines, Curtin University
Erkan Topal: Western Australian School of Mines, Curtin University
Ping Chang: Western Australian School of Mines, Curtin University

Mineral Economics, 2022, vol. 35, issue 2, No 8, 295-307

Abstract: Abstract Understanding the interdependency of commodity market pricing system is very important for running a successful mining business. Much of the iron ore price is derived from the prices of other commodities. This study investigates the relationship between monthly iron ore prices against 12 other monthly commodity prices or indices including LNG, aluminium, nickel, silver, Australian coal, zinc, gold, oil, tin, copper, lead, and Commodity Price Index (Metals) in both bivariate and multivariate perspectives. An augmented Dickey-Fuller (ADF) test is carried out to ensure that all the time series commodity prices and index are non-stationary. In multivariate modelling co-integration tests, observation is made on how many co-integrations exist out of 12 co-integrations for each respective lag between 0 and 45 months’ period. It is observed that 6 out of 12 commodity prices follow co-integrations in 1-month lag and continues in a cyclic pattern until 27 months after which it disappears. There are 3 commodities which continuously co-integrate with iron ore price change at all lags. For bivariate modelling, vector error correction model (VECM) estimation is carried out to prove the evidence of short-run responses to long-term relationship between iron ore prices and it is observed that oil, copper, and Australian coal prices have influence on and from iron ore prices. Then, Granger causality test is carried out to verify the VECM result by testing bi-directional causality between iron ore prices and copper, oil, and coal prices. It has been concluded that the iron ore price has bi-directional influence on oil, copper, and Australian coal prices and vice versa.

Keywords: Iron ore price; Major commodity prices; Vector error correction model; Johansen co-integration; Granger causality (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s13563-022-00301-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:minecn:v:35:y:2022:i:2:d:10.1007_s13563-022-00301-x

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/13563

DOI: 10.1007/s13563-022-00301-x

Access Statistics for this article

Mineral Economics is currently edited by Magnus Ericsson and Patrik Söderholm

More articles in Mineral Economics from Springer, Raw Materials Group (RMG), Luleå University of Technology
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().