Modeling commodity prices for valuation and hedging of mining projects subjected to volatile markets
Bibliographic record
Abstract
Duration between discovery of a mineral deposit and delivery of the material (e.g. metal or concentrate) yielded from this deposit to the market can take several years. After the discovery, a feasibility study is conduced to see if the mining operation on this deposit is economically viable. This feasibility study is exposed to two types of uncertainties, which add to risks to the mining project. These are (1) technical risks arising from sparse data (e.g. grade, recovery and geotechnical characterization) and (2) financial risks arising from unknown future events (e.g. commodity price, discount rate and exchange rate). Among the others, commodity price is a significant concern for the executives of mining enterprise. Given that mining products and their derivatives are traded in commodity, stock and future markets, market dynamics are very complex. Furthermore, it is very sensitive to politics of global world and very open to speculation and manipulation as well as demand and supply. In the past, the mining industry witnessed that many mining operations were suspended or ceased due to unresponsiveness to price fluctuations. Project valuation based on log-term price is a quite naive approach at present day. This can jeopardize the financial resources of the investor company. Therefore, the risks associated with commodity price are assessed, quantified, mitigated, diversified or managed. The analysis of commodity prices starts with the study of historical transactions in financial markets. To facilitate the analysis, it is often necessary to convert commodity prices into returns. Then, the next task is to model the distribution of returns using a statistical distribution. One of the main characteristics of the distribution of commodity price returns is that it tends to have excess kurtosis. This can be explained either by a stochastic volatility or jump component in the diffusion equation describing the evolution of prices. For this reason, it is necessary to consider other models than the Geometric Brownian Motion and Mean-Reverting price models when modeling the dynamics of commodity prices. The objective of this thesis is to construct a robust workflow capable of reproducing the observed price dynamics in the commodity markets. With such calibrated models, it is possible to value mining projects or estimate their exposure to market risk. In the first case, the valuation process is made in a risk-neutral framework using a Real Options approach. In the second case, real world probabilities are used to simulate commodity price paths and assess how a mining project may be exposed to market price fluctuations. Following an introduction and a Literature review, the thesis is divided in four additional parts, corresponding to four different publications. In the first publication, the use of robust estimators for the detection and mitigation of outliers is investigated. The paper starts with an overview of multiple linear regression and assess how the model assumptions can be violated. The second part of the paper deals with detecting outliers the Mahalanobis distance. Then robust regression is used to diminish the effects of outliers in mining engineering data including price. The second paper investigates how the dynamics of iron ore future can be modeled with a dynamic linear model. Traditionally, iron ore futures have been traded using long-term commitment contracts. This paper investigates how relatively recent financial instruments such as futures on iron ore can affect the NPV profile of an iron ore project. The third paper deals with the optimization of the parameters in a commodity model using a genetic algorithm. With correctly calibrated parameters, Monte Carlo simulations of commodity spot and futures are performed and an active trading strategy is implemented in an NPV valuation framework. The last publication deals with the choice of the stochastic process when measuring market risk of a mining project.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".