Volatility estimation for stochastic project value models

Brandão, Luiz Eduardo Teixeira; Dyer, James S.; Hahn, Warren J.

doi:10.1016/j.ejor.2012.01.059

Cited by 46 publications

(45 citation statements)

References 14 publications

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“…Apart from its role in option pricing, the knowledge of volatility allows both producers of minerals and consumers alike to anticipate and take actions against undesired fluctuations in commodity prices and also to quantify the risk involved in each decision . Also, it is the volatility that establishes the degree of uncertainty that can be tolerated by the investor (Brandao et al, 2012). The uncertainty associated with the volatility of selected commodities such as zinc, tin, nickel, copper and oil is presented in Table 3.5 from data obtained from The London Metal Exchange and the Energy Information Administration of the United States of America from the years 1998 to 2000 at a period where government bonds had a volatility of 8% .…”

Section: Review Of Existing Methodsmentioning

confidence: 99%

“…Subsequently, they updated their approach in Lima and Suslick (2006) to include other components of the cash flow, such as operating cost. Brandao et al (2012) introduced the application of fixed cost in their volatility model using the Monte Carlo simulation technique.…”

Section: Review Of Existing Methodsmentioning

confidence: 99%

“…Based on this background, Davis (1998) empirically proved the non constant nature of volatility. Through his work, subsequent researchers have been able to develop models that estimate the volatility of projects quantitatively, moving away from the initial assumption that the volatility of underlying commodities is equal to the volatility of mineral projects Brandao et al, 2012).…”

Section: Project Volatility and Real Optionsmentioning

confidence: 99%

“…Discussion on the gaps in Brandao, Dyer, and Hahn (2012) In this research, the initial assumption that σ S = σ F = σ V was proven not to be valid in all cases of volatility estimation, C(t) = cS (t). This was well expressed in Davis (1998) where σ V = σ S , where the volatility of the underlying asset was expressed as a factor of the project volatility.…”

Section: The Volatility Model Of Davis (1998)mentioning

confidence: 99%

“…• assessed the work of Davis (1998), Lima and Suslick (2006) and Brandao et al (2012) to review the estimation of the volatility of mineral projects. The volatility is an important parameter in the pricing of options.…”

Section: Chapter Summarymentioning

confidence: 99%

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Reasons why Real Options Analysis (ROA) is not widely adopted in the mineral industry

Ampofo¹

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Real options analysis (ROA) is an advanced technique for valuing mineral projects that generates more useful information for decision-makers in the mining industry than that generated using discounted cash flow (DCF) method, yet the latter is much more commonly used in the industry. Why?The answer to this question motivates the thesis.The slow adoption of ROA in the mineral industry has been investigated by several researchers.The outcomes of these previous studies have had minimal impact on the industry because these researchers adopted an approach that focused solely on the quantitative shortcomings of ROA. Until now, little empirical evidence existed on the qualitative reasons that impede the mainstream adoption of ROA as a valuation technique in the mineral industry in spite of its well-established advantages.This thesis advances a series of innovative solutions to address this gap. First, it develops a novel system approach to ROA based on practical assumptions. Secondly, it applies this system approach to a real mine case study to identify the value-added information generated by the ROA method compared with the DCF method. Finally, based on the case study output, the study engages industry experts to identify the qualitative reasons for the relatively low uptake of ROA in the mining industry.In developing the novel system approach to applying ROA, the study focused on building a framework that incorporates realistic modelling of project uncertainties, careful modelling of the value of mineral assets and development of a user-friendly end-user tool that simplifies the process. The system real options (SRO) tool was developed using Matlab to estimate the economic value of projects using ROA. SRO reduces the complexity in ROA application and integrates the method with Monte Carlo simulation and statistical distributions to enable mineral projects under uncertainty to be realistically valued and optimised.To identify the additional information that ROA generates over the DCF method in mineral project valuation, SRO was used to re-value a gold mining project, Adinkra Mine, initially valued using the DCF method in 2004. The initial DCF method valued the project at $493 million; however, SRO recorded a $528 million mean project value with a 62% probability of the project returning a higher value than the DCF value. In addition, the value of the option to expand the mine's capacity was i analysed using the SRO tool to guide the company in its strategic decision-making. Subsequently, the company's country expansion program was assessed using SRO to determine the value of each project under different in-country scenarios. The results of the DCF and ROA were statistically compared with the actual (real) project outputs from 2004 to 2015. The results indicated a higher correlation between the actual (real) project outputs recorded from 2004 to 2015 and the valuation results recorded using the ROA method. In addition, using the ROA approach enabled useful information to be generated that showed significant pot...

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Section: Review Of Existing Methodsmentioning

confidence: 99%

Section: Review Of Existing Methodsmentioning

confidence: 99%