The Development of Applied Real Options Approaches for the Valuation of Mining Projects

与工业中通常使用的标准贴现现金流方法相比，实物期权分析(ROA)被认为是量化现实世界投资机会价值的一种更好的方法，在现实世界中，管理灵活性可以影响这些机会的价值。石油、天然气或矿产开采项目的综合ROA可以改善资本配置和管理决策，这种方法目前在某种程度上被用于商品开采部门。然而，试图解释许多风险因素的现实模型在数学上可能是复杂的，在可能出现许多未来结果的情况下，可能需要进行多个层次的分析。通常情况下，经理们通常无法理解这些模型，并对他们认为不直观的结果不屑一顾。最近的一项经验研究表明，如果经理们做出了更好的时机和产能决策，采矿业务的价值可以增加约35%。 该提案的重点是制定一种“智能”实物期权估值(IROV)方法，以实际应用为目标，特别强调采矿融资。拟议研究的总体目标是开发一种方法和工具，使管理人员和工程师能够在采矿项目估值的背景下，在复杂的、现实的环境中应用ROA。该方法将以利用ROA的稳健财务原则为基础，并将考虑与经济因素相关的系统性风险以及与采矿相关的特殊风险，如矿藏规模和开采难度(成本)。虽然方法论必须考虑到现实世界的复杂性，但它必须是普遍的和广泛适用的。最后，方法必须使管理人员和工程师容易理解其基本原理，以便其使用容易被采用。 提出的模型的一个关键创新是，基于模拟的随机过程来拟合最优决策边界以优化期望值，这些随机过程代表了与矿山相关的重要不确定因素。最终，该模型将由超级飞机组成，管理者可以使用这些超级飞机来决定何时建造、扩建、封存或废弃。提出了两种确定这些边界的方法：1)使用标准曲线拟合和优化技术；2)利用机器学习，其中算法用于优化管理决策，就像在国际象棋等游戏中利用算法来挑战对手一样。据我所知，这里提出的方法尚未提出，这可能是实际执行居留权的一个关键方面。从培训的角度来看，这项研究是多学科的，将为学员提供学习金融建模、挖掘、数据分析、优化和机器学习等方面的机会。

Real option analysis (ROA) is recognized as a superior method to quantify the value of real-world investment opportunities where managerial flexibility can influence their worth, as compared to standard discounted cash-flow methods typically used in industry. A comprehensive ROA of an oil, gas or mineral mining project can improve the allocation of capital and managerial decision making and the methodology is currently used, to some degree, in the commodity extraction sectors. However, realistic models that try to account for a number of risk factors can be mathematically complex, and in situations where many future outcomes are possible, many layers of analysis may be required. Typically, managers are usually unable to understand the models and dismiss results that seem unintuitive to them. A recent empirical study showed that roughly 35% more value can be extracted from mining operations if managers had made better timing and capacity decisions. The focus of this proposal is the development of an “intelligent” real options valuation (IROV) methodology geared towards practical use with specific emphasis on mining finance. The overall objective of the proposed research is to develop a methodology and tool that allows managers and engineers to apply ROA in complex, real world settings in the context of the valuation of mining projects. The methodology will be based on sound financial principles utilizing ROA, and will account for both systematic risks associated with economic factors, as well as idiosyncratic risks associated with mining, such as size of deposit and ease (cost) of extraction. While the methodology must account for real world complexities, it must be general and broadly applicable. Finally, the methodology must be such that its fundamentals are easily understood by managers and engineers so that its use is readily adopted. A key innovation of the model being proposed is the idea of fitting optimal decision making boundaries to optimize the expected value, based on simulated stochastic processes that represent important uncertain factors associated with the mine. Ultimately, the model will consist of hyper-planes that can be used by managers to decide when to build, expand, mothball or abandon. Two approaches to determine these boundaries are proposed: 1) utilizing standard curve fitting and optimization techniques, and 2) utilizing machine learning where an algorithm is utilized to optimize managerial decision making, in the same way as algorithms are utilized to challenge opponents in games such as chess. To the best of my knowledge, the methodology proposed here has not been presented and may provide a key aspect in the practical implementation of ROA. From a training perspective, the research is multi-disciplinary and will provide trainees the opportunity to learn aspects of financial modelling, mining, data analysis, optimization and machine learning.