Abstract: Among the many sources of uncertainty in mining are production incidents: these can be strikes, environmental issues, accidents, or any kind of event that disrupts production. In this work, we present a strategic mine planning model that takes into account these types of incidents, as well as random prices. When confronted by production difficulties, mines which have contracts to supply customers have a range of flexibility options including buying on the spot market, or taking material from a stockpile if they have one. Earlier work on this subject was limited in that the optimization could only be carried out for a few stages (up to 5 years) and in that it only analyzed the risk-neutral case. By using decomposition schemes, we are now able to solve large-scale versions of the model efficiently, with a horizon of up to 15 years. We consider decision trees with up to 615 scenarios and implement risk aversion using Conditional Value-at-Risk, thereby detecting its effect on the optimal policy. The results provide a “roadmap” for mine management as to optimal decisions, taking future possibilities into account. We present extensive numerical results using the new sddp.jl library, written in the Julia language, and discuss policy implications of our findings.

Abstract: We show that traditional measures of curvature and symmetry of the “smiles” improve volatility predictions in forex markets. We consider post crisis data at a daily basis for seven currencies vis a vis the American dollar: The British pound, the Euro, the Australian dollar, the Japanese yen, the Brazilian real and the Mexican and Chilean peso. While our results are robust to the option currency and maturity, they are particularly strong for latin-American currencies and options with longer maturity. We find that the simultaneous inclusion of skewness and kurtosis to a forecasting model significantly improves its predictive accuracy.

Abstract: The purpose of this study is to generate efficient policies for the selection and postponement of copper sales contracts by a mining company. To do so, it uses a two-stage stochastic programming model that determines solutions considering different contract types, random prices, and risk aversion. The results show how it is possible for the selection to involve the lowest risk possible for different revenue levels required. During a period of high price volatility, an efficient solution may deliver an increase in monthly revenue of US$210,000 for a mining company that produces 50,000 tons per year, without any additional risk.

Abstract: We propose an extraction schedule for the Chuquicamata underground copper mine in Chile. The schedule maximizes profits while adhering to all operational and geomechanical requirements involved in proper removal of the material. We include extraction capacity uncertainties due to failure in equipment, specifically to the overland conveyor, which we find to be the most critical component in the extraction process. First we present the extraction plan based on a deterministic model, which does not assume uncertainty in the extraction capacity and represents the solution that the mine can implement without using the results of this study. Then we extend this model to a stochastic setting by generating different scenarios for capacity values in subsequent periods. We construct a multistage model that handles economic downside risk arising from this uncertainty by penalizing plans that deviate from an ex ante profit target in one or more scenarios. Simulation results show that a stochastic-based solution can achieve the same expected profits as the deterministic-based solution. However, the earnings of the stochastic-based solution average 5% more for scenarios in which earnings are below the 10th percentile. If we choose a target 2% below the expected profit obtained by the deterministic-based solution, this average increases from 5% to 9%.

Abstract: One key assumption of Markowitz's model is that all traders act as price takers. In this paper, we extend this mean-variance approach in a setting where large investors can move prices. Instead of having an individual optimization problem, we find the investors' Nash equilibrium and redefine the efficient frontier in this new framework. We also develop a simplified application of the general model, with two assets and two investors to shed light on the potential strategic behavior of large and atomic investors. Our findings validate the claim that large investors enhance their portfolio performance in relation to perfect market conditions. Besides, we show under which conditions atomic investors can benefit in relation to the standard setting, even if they have not total influence on their eventual performance. The 'two investors-two assets' setting allows us to quantify performance and do sensitivity analysis regarding investors' market power, risk tolerance and price elasticity of demand. Finally, for a group of well known ETFs, we empirically show how price variations change depending on the volume traded. We also explain how to set up and use our model with real market data. (C) 2016 Elsevier B.V. All rights reserved.

Abstract: Centralized energy auctions for long-term contracts are commonly-used mechanisms to ensure supply adequacy, to promote competition, and to protect retail customers from price spikes in Latin America. In Chile, the law mandates that all distribution companies must hold long-term contracts – which are awarded on a competitive centralized auction – to cover 100% of the projected demand from three to fifteen years into the future. These contracts can be indexed to a series of financial parameters, including fossil fuel prices at reference locations. Drawing from portfolio theory, we use a simple example to illustrate the difficulties of selecting, through the current clearing mechanism that focuses on average costs and individual characteristics of the offers, a portfolio of long-term energy contracts that could simultaneously minimize the expected future cost of energy and limit the risk exposure of retail customers. In particular, we show that if the objective of the regulator is to limit the risk to regulated consumers, it could be optimal to include contracts that would not be selected based on individual characteristics of the offers and a least-cost auction objective, but that could significantly reduce the price variance of the overall portfolio due to diversification effects between indexing parameters.

Abstract: We examine the unexplored effects on art markets of artist death (asset supply constraints), collectors' wealth (background risk) and forgery risk (risk of investment fraud), under short-sale constraints and risk aversion. Speculative bubbles emerge and have the form of an option strangle (a put option and a call option), in which strike prices are affected by art supply constraints and the association of the artworks' emotional value with both collectors' wealth and forgery, while the options' underlying asset is the stochastic heterogeneous beliefs of agents. We show that speculative bubbles increase with four elements: art supply constraints; a more negative correlation between collectors' wealth and the artworks' emotional value; a more positive relationship between forgery and the artworks' emotional value; and more heterogeneous beliefs. These four sources of speculation increase the expected turnover rate; however, they also augment the variance of speculative bubbles, which generates price discounts (i.e. risk premiums) for holding artworks. Consequently, the net impact of speculation is not necessarily increased art prices. This study not only contributes to the art market literature, but also to studies about speculative bubbles in other financial markets under heterogeneous beliefs, short-sale constraints and risk-averse investors, since we additionally consider the simultaneous effect of asset supply constraints, investors' background risk and the risk of investment fraud.