Mine project evaluation : a real options approach with least-quares Monte-Carlo simulations

Le but de cette thèse porte sur la compréhension et la résolution des problèmes décisionnels rencontrés durant la mise en place de la stratégie d' extraction minière tout en tenant compte des limitations des outils existants sur le marché pour l' optimisation de la valeur économique du site minier. Dans cette thèse, l'auteur amène une nouvelle approche pour évaluer correctement des zones minières avec des incertitudes relatives aux prix des métaux pour des cas réels et concrets. Ceci est destiné aux gestionnaires qui souhaitent utiliser un outil supplémentaire qui est à la fois pratique et compréhensif afin qu'ils établissent des plans d'affaires complets

Mineral Asset Valuation Under Economic Uncertainty: A Complex System for Operational Flexibility

The global mineral industry faces constant challenges that are incited and intensified by market uncertainty. Demand constrictions, resource scarcity, and market volatility all generate market risk that is compounded by the high capital and long payback periods inherent to mining projects. Quantitative risk assessments provide a methodology to leverage uncertain economic scenarios and accurately assess profitability; however, current mine valuation techniques and engineering economic approaches tend to scrutinize the uncertainty of technical factors, such as ore grade and metallurgical recovery, to a much greater degree than market factors, like price-demand restrictions. Nevertheless, the optimal operating conditions for mining, mineral processing and refining must reflect the true dynamics of uncertain commodity prices, and typical operational responses, such as modifications to mine production and material stockpiling.;This thesis presents a new mineral asset valuation methodology based on economic uncertainty in the commodity market and operational flexibility for mining operations. This novel valuation approach resulted in the generation of a complex system that consists of three primary components. First, a price forecasting component was used to generate future commodity price scenarios with two different stochastic differential equation models (Geometric Brownian Motion and Mean-Reverting-drift). Second, a dynamic methodology of discounted cash flow (DCF) was developed, allowing operational flexibility for mining, processing, stockpiling, and selling material. Third, two distinct optimization techniques (Interior-point method and genetic algorithms) were applied for identification of optimal operating parameters in a mining operation, with a particular focus on using buffer stockpiles to ameliorate the impacts of volatile price fluctuations. The dynamic model was applied in a case study assessing the valuation of a greenfield Ni-Co-Sc mine project. The hypothetical deposit was subjected to different levels of commodity price trends, price volatility, discount rates and maximum stockpiling capacity. Overall, the dynamic valuation model obtained NPV results ranging from 2% to 11% higher than standard static DCF techniques. Operational flexibility and ore inventory management proved to be crucial for profit increase on the project

Techniques for Analysing and Reconciling the Progressive Mineral Taxation Regime of Papua New Guinea

The study investigated how progressive tax instruments behave to raise revenues from the mining industry without distorting the investment decision-making. The study used real option and Monte Carlo simulation cash flow tax models and historical data to investigate the revenue collecting potentials of Papua New Guinea’s (PNG) progressive mineral taxation regime. The results show that PNG and mineral endowed nations can successfully capture high magnitude of revenues from the resources sector by making tax instruments more progressive

Assessing investment strategies in mining projects in the Asia-Pacific region

The Asia-Pacific region has experienced a significant period of development over the last four decades. Rapid urbanisation has resulted in an increased demand for mineral resources indicating the resource industry has contributed the primary income to the economies of many Asia-Pacific countries. The objective of this thesis is to shed light on investment opportunity using the strategy of timing flexibility. This thesis uses two methodologies, namely Net Present value (NPV) and real options valuation (ROV), to conduct an investment analysis assessing timing flexibility. This thesis finds that commodity prices affect the mining investors’ decisions. However, the impact of tax policy uncertainty is quite subtle

Pricing of risk and volatility dynamics on an emerging stock market: evidence from both aggregate and disaggregate data

This study analyses risk-return trade-off and behaviour of various volatility dynamics including: volatility, its persistence, mean reversion and speed of mean reversion along with asymmetry and leverage effect on the Pakistani stock market by employing aggregate (aggregate market level) and disaggregate (sectoral level) monthly data for the period from 1998 to 2012. Three generalised autoregressive conditional heteroscedasticity models were applied: GARCH (1,1) for various volatility dynamics; EGARCH for asymmetric and leverage effect and GARCH-M for pricing of risk. The outcomes of the study are as follows: first, the volatility shocks are quite persistent but with varying degrees across the sectors. Both the ARCH effect (short-term effect) and GARCH effect (long-term effect) play their role in generating conditional future stock returns volatility. Further, overall the volatility process is mean reverting; however, the speed of mean reversion varies across the sectors. Secondly, the current study finds little evidence of asymmetry and leverage effect at both aggregate and disaggregates data. Thirdly, the pricing of risk (positive risk premium) is also evident, particularly at the disaggregate data in the Pakistani stock market. Finally, this research study sets the implications for both the policy makers and investors

Quantitative Techniques for Spread Trading in Commodity Markets

This thesis investigates quantitative techniques for trading strategies on two commodities, the difference of whose prices exhibits a long-term historical relationship known as mean-reversion. A portfolio of two commodity prices with very similar characteristics, the spread may be regarded as a distinct process from the underlying price processes so deserves to be modeled directly. To pave the way for modeling the spread processes, the fundamental concepts, notions, properties of commodity markets such as the forward prices, the futures prices, and convenience yields are described. Some popular commodity pricing models including both one and two factor models are reviewed. A new mean-reverting process to model the commodity spot prices is introduced. Some analytical results for this process are derived and its properties are analyzed. We compare the new one-factor model with a common existing one-factor model by applying these two models to price West Texas Intermediate (WTI) crude oil, and discuss its advantages and disadvantages. We investigate the recent behavioral change in the location spread process between WTI crude oil and Brent oil. The existing three major approaches to price a spread process namely cointegration, one-factor and two-factor models fail to fully capture these behavioral changes. We, therefore, extend the one-factor and two-factor spread models by including a compound Poisson process where jump sizes follow a double exponential distribution. We generalize the existing one-factor mean-reverting dynamics (Vasicek process) by replacing the constant diffusion term with a nonlinear term to price the spread process. Applying the new process to the empirical location spread between WTI and Brent crude oils dataset, it is shown how the generalized dynamics can rigorously capture the most important characteristics of the spread process namely high volatility, skewness and kurtosis. To consider the recent structural breaks in the location spread between WTI and Brent, we incorporate regime switching dynamics in the generalized model and Vasicek process by including two regimes. We also introduce a new mean-reverting random walk, derive its continuous time stochastic differential equation and obtain some analytical results about its solution. This new mean-reverting process is compared with the Vasicek process and its advantages discussed. We showed that this new model for spread dynamics is capable of capturing the possible skewness, kurtosis, and heavy tails in the transition density of the price spread process. Since the analytical transition density is unknown for this nonlinear stochastic process, the local linearization method is deployed to estimate the model parameters. We apply this method to empirical data for modeling the spread between WTI crude oil and West Texas Sour (WTS) crude oil. Finally, we apply the introduced trading strategies to empirical data for the location spread between WTI and Brent crude oils, analyze, and compare the profitability of the strategies. The optimal trading strategies for the spread dynamics in the cointegration approach and the one-factor mean-reverting process are discussed and applied to our considered empirical dataset. We suggest to use the stationary distribution to find optimal thresholds for log-term investment strategies when the spread dynamics is assumed to follow a Vasicek process. To incorporate essential features of a spread process such as skewness and kurtosis into the spread trading strategies, we extend the optimal trading strategies by considering optimal asymmetric thresholds

The Economics of Waste Clean-Up from Resource Extraction Projects: Environmental Bonds versus Strict Liability

This thesis contains three essays spanning the fields of environmental economics and investment in a non-renewable resource under uncertainty. All essays relate to the analysis of the clean-up of hazardous waste resulting from natural resource extraction. The first essay addresses the problem of inadequate hazardous waste clean-up by resource extraction firms. It compares the impacts of an environmental bond and a strict liability rule on a firm's ongoing waste abatement and eventual site clean-up decisions. The firm's problem is modeled as a stochastic optimal control problem that results in a system of Hamilton Jacobi Bellman equations. The model is applied to a typical copper mine in Canada. The resource price is modelled as a stochastic differential equation, which is calibrated to copper futures prices using a Kalman filtering approach. A numerical solution is implemented to determine the optimal abatement and extraction rates as well as the critical levels of copper prices that would motivate a firm to clean up the accumulated waste under each policy. We have found that the effect of an environmental bond relative to the strict liability rule depends on certain key characteristics of the bond - in particular whether the bond pays interest and whether the firm borrows at a premium above the risk-free rate to fund the bond. If the firm can borrow at the risk-free rate, and if the government pays the risk-free interest rate on the bond, the value of the mine prior to construction, optimal abatement rates, and optimal operating decisions are the same under the bonding policy and strict liability rule. In contrast, if no interest is paid on the bond, the value of the project is reduced compared to the strict liability rule and the firm undertakes a larger amount of waste abatement under the bond. Because the mine is less pro table, it is less likely that the firm will invest in this mine. In the more realistic case that the firm borrows to fund the bond at a premium over the risk-free rate, the value of mine is reduced further and waste abatement levels are increased. The prospect of investment in the mine is even less likely compared to the previous case. The model developed in the first essay allows that the firm temporarily mothballs the project, but eventually clean-up must occur at the end of the project life. However, the possibility of firm bankruptcy was not explicitly included in that model, and thus mothballing is the only option available to the firm to delay waste clean-up. The second essay contributes to our previous study by considering another important option available to the firm, i.e., the possibility of declaring bankruptcy. A firm's decision to declare bankruptcy is specified as a Poisson process that treats bankruptcy as an exogenous, risky event governed by a hazard rate. The hazard rate at a project level depends on waste stock and output prices, while at the company wide level depends on the commodity prices only. For both default scenarios, the paper demonstrates that the firm operating under a bonding policy, that covers the full cost of waste clean-up, is less able to avoid its liability costs, particularly if the bond is financed from retained earnings. If the firm borrows to finance the bond, it is possible that the firm avoids clean-up costs by defaulting on the loan following a bankruptcy. In contrast to the results of the first essay, if the firm finances the bond out of its retained earnings, and if the government pays the risk-free rate of interest on the bond, the bond and the strict liability rule do not give the same outcome when bankruptcy is possible. Such a bond encourages a higher abatement rate and makes site clean-up more likely compared to the strict liability rule. Firms operating under the liability rule have stronger incentives to delay their clean-up costs by sitting idle and they may eventually go bankrupt at the mothballed stage. Therefore, the possibility of bankruptcy makes the firm worse off under the bonding policy, while benefits the firm under the strict liability rule. Modelling uncertain commodity prices is a key component of the analysis of optimal firm behavior in hazardous waste clean-up. The third essay investigates the dynamics of copper prices by comparing and contrasting three different stochastic models, which are a one-factor mean-reverting model, a two-factor model, and a one-factor long-term model. These models are calibrated to copper futures prices using a Kalman filtering approach. The first model assumes spot prices are mean-reverting in drift. The second model defines two correlated stochastic factors that are spot prices and convenience yield. The third model transforms the two-factor price model into a single factor model. We have found that the first model fails to describe the term structure of copper futures prices with long maturities. In contrast, the two-factor and the long-term models are shown to provide a reasonable fit of the term structure of copper futures prices and can be applied to long-term investment projects. The results highlight the importance of stochastic convenience yield in copper price formation

Essays on the Dynamics of Capital Structure

Tests of the static trade-off theory that posits that firms move towards the optimum capital structure necessitate a joint hypothesis test - whether firms adjust toward target leverage, and whether the proxy used for target leverage is the true target leverage. Prior studies use the time-series mean leverage for each firm, the industry median leverage, an estimated cross-sectional leverage, and a tobit estimated leverage using the factors suggested by the static trade-off theory as proxies for the target leverage. In this dissertation, I examine whether these proxies are equivalent and test the consistency of the proxies with the theorized behavior of the true target leverage. My results indicate that the four proxies we examine have significantly different distributions and this holds across most industries. Further, the industry median leverage is the proxy which best exhibits behavior consistent with the true target leverage. Firm value is higher for firms closer to the industry median and lower for firms away from the industry median. A robustness check using Kmeans cluster analysis confirms the superiority of the industry median leverage over the other proxies of target leverage. This study complements the previous studies on the pecking order theory and the trade-off theory. The main purpose of this study is to investigate three issues that are not considered in the previous studies. The adequacy of the specification and the assumptions of the models used in testing the trade-off and the pecking order theory. The second issue examined in this study is the validity to putting the pecking order and the trade-off theories in a horse race. The final issue examined in this study is the factors driving firms to issue (repurchase) debt or equity or combination of both and simultaneously the factors affecting the size of issue (repurchase

Application of the Real Options in Engineering Design and Decision Making: Focus on Mine Design and Planning at Operational Level

Flexibility and adaptability are essential for long-term corporate success, and real options (RO) is the preferred tool for analysis. This research argues that uncertainty is a source of value as the opportunities that it presents can be leveraged by having a flexible system. As a contribution to knowledge, a relationship between the beta and flexibility index was derived, RO identification framework for mine operational decision-making was proposed and predictive data analytics was utilised to create managerial flexibility

How to Rate the Financial Performance of Private Companies? A Tailored Integrated Rating Methodology Applied to North-Eastern Italian Districts

This paper contributes to solving the puzzle of assessing the financial performance of private/unlisted companies. The inner characteristics of these companies make the adoption of traditional best practices in estimating risk premia difficult or impossible. Moreover, the lack of market data and comparable information biases the perception of corporate performance and generates the misallocation of credit fundings (both quantities and pricing). Hence, in this paper, we develop an Integrated Rating Methodology (IRM) to estimate a more efficient corporate “return-to-risk” measure. Our IRM is rooted in the seminal “certainty equivalent” model as developed by Lintner in 1965, but we modify it using a shortfall approach, and then compute a “confident equivalent” that is compliant with Fischer Black’s zero-beta model as well as the Basel agreements. An empirical application of the approach is conducted with a sample of 13,583 non-financial SMEs in the north-east regions of Italy, where there is evidence of inefficient bank financing. We back-test our IRM by rating these companies using corporate financial data during the period 2007–2014, which encompasses both the Great Financial Crisis and the European sovereign debt crisis. Our empirical results depict a clear crowding-out effect of credit allocations when we compare our IRM scoring measure with the actual raising ability and the cost of capital relating to these firms. We find that 36% of companies are underfunded, even if they have a superior IRM score, while 27% of them are funded without merit. Interestingly, this last figure is in line with the average non-performing loan ratio provided by official Italian statistics from 2015 to 2020. Therefore, we conclude that our IRM methodology is promising and may be better at estimating risk financing in small private companies (including start-ups) than internal banking models. These initial results will drive our forthcoming research towards creating an IRM 2.0