Dynamic discount rate through Ornstein-Uhlenbeck process for mining project valuation
Bibliographic record
Abstract
Discounted cash flow (DCF) is a conventional and widely used method for the mine valuation. The reason behind its wide use does not require complex mathematical computations. However, the technique is static; it does not allow the practitioner to react to the market changes. In DCF analysis, one of the important input is the discount rate, which, to some extent, represents the financial risk that the mining company may face in the future. The discount rate is sensitive to market and political uncertainties. Therefore, in fact, it is a dynamic parameter varying over the years. The application of DCF method is quite simple. As the life of mine prolongs, since the effect of discounting grows, DCF may not reflect the actual value of the project. This research proposes a way to find the discount rate for each year. The discount rate is calculated by a weighted average cost of capital formula by incorporating into the interest rate. The interest rate is treated by a stochastic process called Ornstein-Uhlenbeck (OU) process. This approach generates multiple realizations for the discount rate. Therefore, the proposed approach can quantify risks associated with the discount rate. Thus, the management of a mining project can make meaningful decisions. In doing so, a case study is conducted on a mining project to demonstrate the proposed approach.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".