Relationship of iron ore price with other major commodity prices
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 month...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
2022
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| Online Access: | http://hdl.handle.net/20.500.11937/89224 |
| _version_ | 1848765181407002624 |
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| author | Kim, Yoochan Ghosh, Apurna Topal, Erkan Chang, Ping |
| author_facet | Kim, Yoochan Ghosh, Apurna Topal, Erkan Chang, Ping |
| author_sort | Kim, Yoochan |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T11:31:10Z |
| format | Journal Article |
| id | curtin-20.500.11937-89224 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:31:10Z |
| publishDate | 2022 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-892242022-09-19T01:14:01Z Relationship of iron ore price with other major commodity prices Kim, Yoochan Ghosh, Apurna Topal, Erkan Chang, Ping 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. 2022 Journal Article http://hdl.handle.net/20.500.11937/89224 10.1007/s13563-022-00301-x http://creativecommons.org/licenses/by/4.0/ fulltext |
| spellingShingle | Kim, Yoochan Ghosh, Apurna Topal, Erkan Chang, Ping Relationship of iron ore price with other major commodity prices |
| title | Relationship of iron ore price with other major commodity prices |
| title_full | Relationship of iron ore price with other major commodity prices |
| title_fullStr | Relationship of iron ore price with other major commodity prices |
| title_full_unstemmed | Relationship of iron ore price with other major commodity prices |
| title_short | Relationship of iron ore price with other major commodity prices |
| title_sort | relationship of iron ore price with other major commodity prices |
| url | http://hdl.handle.net/20.500.11937/89224 |