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

矿业项目估值应用实物期权方法的发展

• 批准号: RGPIN-2017-06627
• 负责人: Lawryshyn, Yuri
• 金额: $ 1.53万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=711451
• 关键词: Development; Applied; Real; Options; Approaches

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.

真实的期权分析（罗阿）被认为是一种上级方法来量化现实世界的投资机会的价值，管理的灵活性可以影响他们的价值，相比标准的贴现现金流方法通常用于行业。石油、天然气或矿产开采项目的全面罗阿可以改善资本分配和管理决策，目前在某种程度上，该方法用于商品开采部门。然而，试图考虑许多风险因素的现实模型在数学上可能很复杂，并且在许多未来结果可能的情况下，可能需要许多层分析。通常情况下，管理人员通常无法理解模型，并忽略对他们来说似乎不直观的结果。最近的一项实证研究表明，如果管理人员能够做出更好的时机和能力决策，那么可以从采矿业务中提取大约35%的价值。 这项建议的重点是发展一种“智能”真实的期权估值方法，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，在复杂的真实的世界环境中应用罗阿。该方法将基于利用罗阿的健全的财务原则，并将考虑到与经济因素有关的系统风险以及与采矿有关的特殊风险，如存款的规模和开采的难易程度（成本）。虽然方法必须考虑到真实的世界的复杂性，但它必须具有普遍性和广泛适用性。最后，该方法必须使管理人员和工程师容易理解其基本原理，以便易于采用。 提出的模型的一个关键创新是拟合最佳决策边界的想法，以优化预期值，模拟随机过程，代表与矿山相关的重要不确定因素。最终，该模型将由管理人员可以用来决定何时建立，扩展，封存或放弃的超平面组成。提出了两种确定这些边界的方法：1）利用标准曲线拟合和优化技术，以及2）利用机器学习，其中利用算法来优化管理决策，就像利用算法来挑战象棋等游戏中的对手一样。据我所知，这里提出的方法尚未提出，但可能是实际实施罗阿的一个关键方面。从培训的角度来看，该研究是多学科的，将为学员提供学习金融建模，挖掘，数据分析，优化和机器学习方面的机会。