Changing patterns of trade in the world iron ore and steel market: An econometric analysis

Labson, B. Stephen

doi:10.1016/s0161-8938(96)00049-x

Cited by 19 publications

(7 citation statements)

References 8 publications

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“…One method of mitigating price volatility risk existent in the literature is to perform future to predict pricing fluctuations. Wårell [8], Pustov et al [9], Morandi et al [10], Labson [11], and Sverdrup At the inception of the project, the feedstock agreement is executed, ensuring a stable supply of mined ores throughout the duration of the project. Theoretically any losses due to low-grade ores is borne by all organizations.…”

Section: Literature Reviewmentioning

confidence: 99%

“…One method of mitigating price volatility risk existent in the literature is to perform future to predict pricing fluctuations. Wårell [8], Pustov et al [9], Morandi et al [10], Labson [11], and Sverdrup and Ragnarsdottir [12] are all examples of attempted to predict the future costs of material ores based on different identified variables. Both Wårell and Marandi et al identified the main factors indicating future of material ore pricing [8,10].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Pustov et al [9] used a marginal cost approach and incentive pricing, supplemented with a Monte-Carlo simulation, to present confidence intervals of future material ore pricings. Labson [11] developed an econometric trade model that evaluates the dynamics of the world steel market, forecasting future market demands. Sverdrup and Ragnarsdottir [12] combined mining, ore grade changes, trade markets, price mechanisms, supply and demand, estimates of stock-in-use, waste, and recycling into a comprehensive model which also forecasted future market demands through 2080.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Existing literature has identified long-term iron ore pricing agreements as a major problem, being too rigid and failing to reflect market demand [40,41]. While literature has acknowledged the difficulties associated with long-term iron ore contractual agreements [19], research within this area has been limited to models calculating future iron pricings [8][9][10][11][12]. These solutions do not consider the integration of fluctuating iron ore pricing into contractual arrangements.…”

Section: Point Of Departurementioning

confidence: 99%

“…From these variables, an LCCA analysis was performed with and without using the model under their three cases. Utilizing LCCA calculation best practices [44][45][46][47][48][49][50], the NPV, internal rate of return (IRR), and profitability index (PI) are calculated using the formulas below: (11) where NPV denotes the net present value (M USD); t denotes number of time periods (in years from 1 to "T" years); C t denotes net cash inflow during period t (M USD); C 0 denotes initial investment costs (M USD); and r denotes the discount rate (in percentage).…”

Section: 4 Life-cycle Cost Analysismentioning

confidence: 99%

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Ore Composition’s Impact on Smelting Profitability: An Optimum Pricing Index Model for Long-Term Nickel Ore Feedstock Purchasing Agreements

Jeong¹,

Alleman

2018

Applied Sciences

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Global Nickel (Ni) smelters’ have been experiencing profit losses for nearly a decade due to the 2008 recession still impacting the industry, oversupply, and fluctuating ore quality. This paper proposes to aid the Ni smelters with the lattermost issue, presenting an optimum pricing index model for purchasing raw Ni ore materials. The model is developed using data from a major Ni smelter in operation in Korea, including parameters such as revenues, investment expenses, and ore purchasing costs and the impact Ni and Iron (Fe) content variation has on them. In contrast, existing published Ni price forecasting models are based on external variables (e.g., GDP) and are intended for profit forecasting versus contractual agreements. In executing a Monte-Carlo simulation of 1000 possible life-cycle costs analysis with and without the use of the proposed model, the model increased the likelihood of the smelter earning a profit by approximately 5% with an average approximate increase of $50 million. As such, the proposed index model provides new Ni and nonferrous metals smelters material quality fluctuation risk mitigation. Although this model is presented for the Ni smelting process, the findings could theoretically be applied to any long-term procurement activity with variable quality and market conditions.

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Section: Literature Reviewmentioning

confidence: 99%

“…One method of mitigating price volatility risk existent in the literature is to perform future to predict pricing fluctuations. Wårell [8], Pustov et al [9], Morandi et al [10], Labson [11], and Sverdrup and Ragnarsdottir [12] are all examples of attempted to predict the future costs of material ores based on different identified variables. Both Wårell and Marandi et al identified the main factors indicating future of material ore pricing [8,10].…”

Section: Literature Reviewmentioning

confidence: 99%