Ore Price Research Articles

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 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.

Performance of Different Models in Iron Ore Price Prediction During the Time of Commodity Price Spike

Performance of Different Models in Iron Ore Price Prediction During the Time of Commodity Price Spike

An Economic Model to Assess Profitable Scenarios of EAF-Based Steelmaking Plants under Uncertain Conditions

The steelmaking processes are considered extremely energy-intensive and carbon-dependent processes. In 2018, it was estimated that the emissions from global steel production represented 7–9% of direct emissions generated by fossil fuels. It was estimated that a specific emissions value of 1.8 tCO2 per ton of steel was produced due to the carbon-dependent nature of the traditional blast furnace and basic oxygen furnace (BF-BOF) route. Therefore, it is necessary to find an alternative solution to the BF-BOF route for steel production to counteract this negative trend, resulting in being sustainable from an environmental and economic point of view. To this concern, the objective of this work consists of developing a total cost function to assess the economic convenience of steelmaking processes considering the variability of specific market conditions (i.e., iron ore price, scraps price, energy cost, etc.). To this purpose, a direct reduction (DR) process fueled with natural gas (NG) to feed an electric arc furnace (EAF) using recycled steel scrap was considered. The approach introduced is totally new; it enables practitioners, managers, and experts to conduct a preliminary economic assessment of innovative steelmaking solutions under market uncertainty. A numerical simulation has been conducted to evaluate the profitability of the investment considering the economic and environmental costs. It emerged that the investment is profitable in any case from an economic perspective. On the contrary, considering the environmental costs, the profitability of the investment is not guaranteed under certain circumstances.

Dynamic spillovers and dependencies between iron ore prices, industry bond yields, and steel prices

We examine the spillovers and dependencies between iron ore prices, bond yields of mechanical equipment, infrastructure construction and housing, and car industries, and steel prices by using the spillover index and copula model over the period 2011–2021. Our results show that compared with bond yields, iron ore prices are primary contributors of price spillovers and highly comparable with China's steel prices. Notably, the magnitudes of correlations and risk transmissions from bond yields of related industries to steel prices shifts over the period before the implementation of China's policy to cut overcapacity in steelmaking, after the policy implementation and before the outbreak of coronavirus, and after the outbreak of coronavirus. Because of high price spillovers from iron ore to steel prices and iron ore-steel relationship, policymakers in the steel industry should consider more supply-side policies to mitigate market risks. Meanwhile, risk-averse investors should increase the share of bonds of related industries.

Endogeneity of commodity price in freight cost models

In this paper, we answer a novel question on how the value of goods carried can affect the freight cost. We focus on the issue based on a more specialized freight market involving transport of seaborne iron ore from mining ports to Qingdao in China during the period 2014 to 2019. We construct simultaneous systems of demand–supply equations on both the iron ore market and the freight market. In the models, we explain how endogeneity of iron ore as a regressor can arise due to the nexus between the two markets, and that the freight demand is largely a derived demand from iron ore demand by PRC firms importing the iron ore. Employing instrumental variable two-stage least squares regression, it is shown that iron ore price negatively affects, ceteris paribus, the freight rate of bulk carriers ferrying the iron ore. Industrial growth, bunker fuel oil, Baltic Dry Index, and transport distance have positive effects on the freight rates.

Holistic Pre-Feasibility Study of Comminution Routes for a Brazilian Itabirite Ore

Comminution is an essential step in processing itabirite ores, given the need to liberate silica and other contaminants from the iron minerals for downstream concentration and then pellet feed production. In general, these ores in Brazil are not particularly hard to crush and grind, but both capital (CAPEx) and operating (OPEx) expenditures in this stage of preparation can be critical for the project, in particular due to uncertainties in iron ore prices. Several circuits have been designed and are in operation for this type of ore in Brazil; however, it is not yet clear which technologies are more cost-effective and in which configuration they should be applied. This work critically analyzes four comminution circuits for an undisclosed case study. For these circuits, CAPEx, OPEx, and some environmental sustainability indices, as well as qualitative technical criteria, were used in the comparisons. This work concludes that two of these process routes, especially those based on more energy-efficient technologies (and one of these still rarely explored even at bench-scale), have demonstrated to be very attractive from multiple standpoints.

Time-varying spillovers and dependencies between iron ore, scrap steel, carbon emission, seaborne transportation, and China's steel stock prices

We have examined the time-varying price spillovers and dependencies between iron ore, scrap steel, carbon emissions, seaborne transportation, and China's steel stock by using price spillover and copula models to cover the period 2011–2020. More specifically, the Platts, the MBIOI, and the Chinese Iron Ore spot prices (CIOPI) are analyzed and compared. Our results show that of the three iron ore price indexes, the Platts is a primary contributor of price spillover and highly comparable with China's steel stock prices. The scrap steel, seaborne transportation, and carbon emission prices incur relatively low price spillovers and moderately link with China's steel stock prices. In addition, the time-varying price spillovers and links are sensitive to economic shocks stemming from China's policy implementation to cut overcapacity in steelmaking and the spread of coronavirus. Our results may suggest that the Platts is still the benchmark spot price in determining China's steel stock prices. The results might also guide market investors to hedge the financial risks in the commodity market and help policymakers design appropriate financialization policies for the iron ore and steel markets.

Impact of Commodity Price on Freight Market Considering the 2008 Crisis: An Investigation of Iron Ore Price and Capesize Shipping Rates

This study investigated the asymmetric causality from iron ore price to freight market through Capesize rates by considering the possible impact of the 2008 global economic crisis. The study used a monthly data set of 233 observations covering the period between January 2000 and June 2019. According to the structural break tests, a break was detected in June 2008 and the analyses were performed for two separate periods as pre-crisis and post-crisis in addition to the whole period. The results for the whole period revealed that positive shocks in iron ore price caused negative shocks and negative shocks caused both negative and positive shocks. The divided sample results revealed that positive shocks in the iron ore prices were causes of the positive shocks in the freight rates in the pre-crisis period, while negative shocks in the ore prices were causes of the negative shocks in freight rates, and negative shocks were the causes positive shocks in the post-crisis period. These results suggested that the impact of commodity prices on the freight market before and after the crisis might vary.

Time-varying effect of international iron ore price on China’s inflation: A complete price chain with TVP-SVAR-SV model

This paper employs a time-varying parameter structural vector autoregression with stochastic volatility (TVP-SVAR-SV) model to examine the time-varying impacts of three types of international iron ore price shocks (the supply, special demand and global demand shock) on China’s inflation from a complete price chain perspective. The empirical results indicate that: (1) demand shocks rather than supply shock in iron ore price have strong effects on China’s inflation at each stage, revealing the importance of distinguishing the underlying sources of price shifts; (2) the effects of iron ore price shocks on China’s inflation at the import stage are substantial and gradually decrease along the price chain. Meanwhile, the product prices of upstream industries are greatly influenced by price shocks, but downstream companies fail to shift rising costs to consumers due to demand elasticity; (3) the fluctuations of iron ore price are not driven by China’s inflation or supply but rather by demand factors, which is in line with the theory of super cycle. Based on the findings, policies focusing on decreasing preventive demand should be proposed. Moreover, China ought to increase overseas investment and improve utilization rates of scrap and iron ore to ease imported inflationary pressures.

Do iron ore, scrap steel, carbon emission allowance, and seaborne transportation prices drive steel price fluctuations?

We examine the sensitivity of China's steel price movements to iron ore, scrap steel, carbon emission allowance, and seaborne transportation prices over the period 2010–2020. More specifically, the price spillover, the dependence structure, and the spillover risk between iron ore, scrap steel, carbon emission allowance, seaborne transportation, and China's steel prices are analyzed in a multivariate framework, vine copula, and conditional risk setup. Our results demonstrate that before and after the implementation of China's policy to cut overcapacity in steelmaking, China's steel prices are mainly subject to iron ore prices. Our results have potential implications for policymakers to exploit market incentives to regulate steelmakers' investments and capacity in steelmaking.

An Analysis of Global Iron Ore Resource Market Trend in the Post-COVID-19 Period

Iron is the most widely used metal in China. There have been many changes in the international iron ore market due to the covid-19. Analyzing the reasons for the changes in the international iron ore supply, demand and market structure and predicting the future trends are of great significance for the stable supply of iron ore. This paper first analyzes the global steel production, iron ore supply and price trends under the covid-19 and believes that the global iron ore supply and demand pattern is further concentrated, showing a pattern of countries, two 60%. That is to say, China's steel production will further improve its global share, approaching 60%;Australia's supply share in the global iron ore shipping market will further increase, approaching 60%, because of the covid-19. Secondly, this paper predicts the changing trend of China's and global steel demand in the next 2-3 years, and believes that the main reason for the increase in China's steel production in recent years is the country's need for stable economic growth. China's steel production will remain high in the next 2~3 years, but in the long run, China's iron ore demand will slow down after a period of time. Finally, this paper analyzes the global iron ore price trend and believes that the global iron ore price will rise and fall to less than 100 US dollars/ton in the fourth quarter of 2020. The iron ore price will slowly fluctuate and fall down to 60~80 US dollars/ton in the next 2~3 years. © 2021, Science Press. All right reserved.

Forecasting the Global Price of Iron Ore Using Neural Networks

Forecasting the Global Price of Iron Ore Using Neural Networks

Forecasting the global price of iron ore using neural networks

Forecasting the global price of iron ore using neural networks

Materiality, inequality, and future‐making as focal points of future engagement of economic anthropology with climate change

Materiality, inequality, and future‐making as focal points of future engagement of economic anthropology with climate change

Pit Optimization for Improved NPV and Life of Mine in Heterogeneous Iron Ore Deposit

Operations planning and management of a large open pit mine is an enormous and complex task, particularly for mines having long life. The long-term planning of open pit mine is a dynamic process and should be reviewed periodically for enhancing the NPV and life of mine. Pit optimization through soft computing can be gainfully applied to resolve a number of important problems that arise in developing suitable planning strategies and management of a mine. Pit optimization starts with the assumption of initial production capacities and the estimates for the related costs and commodity prices. Once the economic parameters are known, the analysis of the ultimate pit limits of the mine is undertaken to determine the portion of the deposit that can be mined economically and safely from geotechnical stability view point. In this research, pit optimization for a heterogeneous iron ore deposit (Deposit-A) has been carried out successfully using Whittle Software. The key aspect investigated was the optimal pit shell across a range of selling prices, as it enables understanding the characteristics of the deposit and potential development strategies considering the areas of greater value. The methodology can be very well utilized when fluctuations in the selling price occur, for predicting the future selling price of the commodity and also strategizing mining plan. For the deposit investigated, it was found that the mine is likely to generate final product of 66.55 MT of Lump Ore (+ 10 mm − 150 mm) and Fines Ore 156.45 MT (− 10 mm) during the life of the mine. The NPV is found to be highly sensitive to the price of Fine ore as quantity of fines in total ore body is around 70%. Except waste, all ore types, no matter how inferior the grade is having (around 10 MT of low grade coming in between Fe @ 45% and Fe @ 25% can be also utilized), are mineable and can be suitably blended to give a product mix of Fe @ 61.68% and more.

Modelling the iron ore price index: A new perspective from a hybrid data reconstructed EEMD-GORU model

As iron ore is the fundamental steel production resource, predicting its price is strategically important for risk management at related enterprises and projects. Based on a signal decomposition technology and an artificial neural network, this paper proposes a hybrid EEMD-GORU model and a novel data reconstruction method to explore the price risk and fluctuation correlations between China’s iron ore futures and spot markets, and to forecast the price index series of China’s and international iron ore spot markets from the futures market. The analysis found that the iron ore futures market in China better reflected the price fluctuations and risk factors in the imported and international iron ore spot markets. However, the forward price in China’s iron ore futures market was unable to adequately reflect the changes in the domestic iron ore market, and was therefore unable to fully disseminate domestic iron ore market information. The proposed model was found to provide better market risk perceptions and predictions through its combinations of the different volatility information in futures and spot markets. The results are valuable references for the early-warning and management of the related enterprise project risks.

Sensitivity Analysis of Net Present Value due to Optimal Pit Limit in PT Ceria Nugraha Indotama, Kolaka Regency, Southeast Sulawesi Province

PT Ceria Nugraha Indotama is planning to open a new mining site in Lapaopao Block area. In order to determine the most profitable pit design on this new area, pit limit optimization is required. Pit limit is optimized by considering economic parameters, geotechnical parameters, and production target. The optimal pit limit generates the highest Net Present Value (NPV) which is affected by economic parameters. The objective of this research is to analyse the NPV sensitivity of pit due to the changes of economic parameters namely mining cost, processing cost, and selling cost by using sensitivity analysis. Data used in this research were drill hole, ore and waste density, slope geometry, production target. The planned production target, ore price, recovery factor, dilution factor, mining cost, processing cost, and selling cost. The analysis results showed when mining costs increased by 20%, NPV decreased from $2,407,032 to $1,683,430 and when mining costs decreased by 20%, NPV increased from $2,407,032 to $3,130,635. When processing cost increased by 20%, NPV decreased from $2,407,032 to $2,133,523 and when processing cost decreased by 20%, NPV increased from $2,407,032 to $2,680,542. When selling cost increased by 20%, NPV decreased from $2,407,032 to $1,995,755 and when selling cost decreased by 20%, NPV increased from $2,407,032 to $2,818,310. It indicated that the economic parameters significantly affect the NPV and most sensitive to the mining cost parameter changes.

From Long-term Contract to Market: An RBC Perspective on International Negotiations of Iron Ore Prices in the Asia-Pacific Region, 2009–2010

Abstract The collapse of a long-term contract-based (LTC) benchmark system and the rise of a market-based index system in international negotiations of iron ore prices in the Asia-Pacific region has attracted much media attention. However, a systematic analysis of why and how such a change occurred from a negotiation point of view is absent. Drawing upon a relationship-behavior-conditions (RBC) perspective from the international business (IB) negotiation literature, this article investigates how negotiations between parties unfolded during the 2009–2010 period. Specifically, the article contributes to a deeper understanding of the subject by evaluating the relationships between various negotiating parties, investigating some intriguing behaviors by negotiating parties, and identifying important conditions surrounding the negotiation process. The case of iron ore price negotiation also offers a vehicle to advance the RBC perspective in untangling complex IB negotiation problems and generate some broad implications for IB negotiation research and practices.

Development of a Group Method of Data Handling Technique to Forecast Iron Ore Price

Iron is one of the most applicable metals in the world. The global price of iron ore is determined based on demand and supply. There are numerous parameters (e.g., price of steel, steel production, oil price, gold price, interest rate, inflation rate, iron production, and aluminum price) affecting the global iron ore price. Considering the high number of effective parameters and existence of complex relationship among them, artificial intelligence-based approaches can be employed to predict iron ore price. In this paper, a new intelligence system namely group method of data handling (GMDH) was developed and introduced to predict the price of iron ore. For comparison purposes, four other techniques i.e., autoregressive integrated moving average (ARIMA), support vector regression (SVR), artificial neural network (ANN), and classification and regression tree (CART) were developed for prediction of monthly iron ore price. Then, using testing datasets, the developed models were validated and their performance capacities were compared. The results showed that performance prediction of the GMDH model is significantly better than other predictive models based on four performance indices i.e., root mean square error, variance account for (VAF), mean absolute error, and mean absolute percentage error. Results of VAF (97.89%, 90.81%, 80.95%, 55.02%, and 23.87% for GMDH, SVR, ANN, CART, and ARIMA models, respectively) revealed that the GMDH technique is able to predict iron ore price with higher degree of accuracy compared to the other techniques.

Government–donor relations in Sierra Leone: who is in the driving seat?

AbstractSince the cessation of conflict in 2002, Sierra Leone has experienced extraordinary levels of involvement from Western donors. Paradoxically, while relationships are often portrayed on the ground as strong with significant donor influence, our research shows considerable fluidity in individual and institutional relationships. The article disaggregates donor–government relations at various levels over a short but crucial period, 2010–16, asking in each case who occupies the driving seat. In so doing, the article interrogates the concept of ‘extraversion’, investigating to what extent government – and indeed donors – has space in which to manoeuvre and how and why government and donors act as they do in this space. The period 2010–16 is of particular interest due to extreme iron ore price volatility and the Ebola epidemic of 2014–15. The article adds much-needed critique and empirical evidence to the debate on donor influence and ‘extraversion’.