Correlation between geology and concentration-volume fractal models: significance for Cu and Mo mineralized zones separation in the Kahang porphyry deposit (Central Iran)

ArticleThis study identifies the major mineralized zones including supergene enrichment and hypogene enrichment in the Kahang Cu-Mo porphyry deposit which is located in Central Iran based on subsurface data and utilization of the concentration-volume (C-V) fractal model. Additionally, a correlation between results achieved from a C-V fractal model and geological models consisting of zonation, mineralography and alteration have been conducted in order to have an accurate recognition and modification of the main mineralized zones. Log-log plots indicate five geochemical populations for Cu and Mo in the deposit which means that mineralization commences with 0.075 % and 13 ppm for Cu and Mo (as the first thresholds) respectively. The main mineralization began for Cu≥0.42 % and Mo≥100 ppm and also enriched mineralization containing Cu≥1.8 % and Mo≥645 ppm which is located in the central part of the deposit. According to the C-V model, the main Cu-Mo mineralized zones occur in the hypogene zone, especially in the central, NW and NE parts of the Kahang deposit. The supergene enrichment zone derived via the C-V model is smaller than that in the geological model and is located in the central and eastern parts of the deposit. Results analysed by the C-V fractal model certify that the interpreted zones based on the fractal model are accurate. To certify this, a logratio matrix has been employed to validate the C-V fractal model for the Cu and Mo main mineralized zones.The authors are grateful to the National Iranian Copper Industries Co. (NICICO) for their permission to have access to the Kahang deposit dataset. Additionally, the authors would like to thank Dr. A. Saad Mohammadi the former CEO of NICICO for his support. The authors would like to thank the reviewers of this paper for their comments and valuable remarks

Non-equidistant “Basic Form”-focused Grey Verhulst Models (NBFGVMs) for ill-structured socio-economic forecasting problems

Multiple uncertainties complicate socio-economic forecasting problems, especially when relying on ill-conditioned limited data. Such problems are best addressed by grey prediction models such as Grey Verhulst Model (GVM). This paper resolves the incompatibility between GVM’s estimation and prediction by taking its basic form equation as the basis of both. The resultant “Basic Form”-focused GVM (BFGVM) is also further developed to create Direct Non-equidistant BFGVM (DNBFGVM) and, in turn, DNBFGVM with Recursive simulation (DNBFGVMR). Experimental analyses comprise 19 socio-economic time series with an emphasis on Iranian population, a low-frequency non-equidistant time series with remarkable strategic importance. Promisingly, the proposed DNBFGVM and DNBFGVMR provide accurate in-sample and out-of-sample socio-economic forecasts, show highly significant improvements over the best traditional GVM, and offer cost-effective intelligent support of decision-making. Final results suggest future trends of studied socio-economic time series. Specifically, they reveal Iranian population to grow even slower than anticipated, demanding an urgent consideration of policy-makers

A new accuracy measure based on bounded relative error for time series forecasting

Many accuracy measures have been proposed in the past for time series forecasting comparisons. However, many of these measures suffer from one or more issues such as poor resistance to outliers and scale dependence. In this paper, while summarising commonly used accuracy measures, a special review is made on the symmetric mean absolute percentage error. Moreover, a new accuracy measure called the Unscaled Mean Bounded Relative Absolute Error (UMBRAE), which combines the best features of various alternative measures, is proposed to address the common issues of existing measures. A comparative evaluation on the proposed and related measures has been made with both synthetic and real-world data. The results indicate that the proposed measure, with user selectable benchmark, performs as well as or better than other measures on selected criteria. Though it has been commonly accepted that there is no single best accuracy measure, we suggest that UMBRAE could be a good choice to evaluate forecasting methods, especially for cases where measures based on geometric mean of relative errors, such as the geometric mean relative absolute error, are preferred

Portfolio selection with robust estimators considering behavioral biases in a causal network

In this study, we develop a behavioral portfolio selection model that incorporates robust estimators for model inputs in order to reduce the need to change the portfolio over consecutive periods. It also includes Conditional Value at Risk as a sub-additive risk measure, which is preferable in behavioral portfolio selection. Finally, we model a varying risk attitude in a causal network in which investor behavioral biases and latest realized return are related to using a causation algorithm. We also provide a case study in Tehran Stock Exchange, where the results disclose that albeit our model is not mean-variance efficient, it selects portfolios that are robust, well diversified, and have less utility loss compared to a well-known behavioral portfolio model

Comparing the performance of GARCH (p,q) models with different methods of estimation for forecasting crude oil market volatility

Abstract In recent years, empirical literature used widely GARCH models to characterize crude oil price volatility. Because this augmenting attention, six univariate GARCH models and two methods of estimation the parameters for forecasting oil price volatility are examined in this paper. Based on obtained results, the best method for forecasting crude oil price volatility of Brent market is determined. All the examined models in this paper belong to the univariate time series family. The four years out-of-sample volatility forecasts of the GARCH models are evaluated using the superior predictive ability test with more loss functions. The results show that GARCH (1,1) can outperform all of the other models for the crude oil price of Brent market across different loss functions. Four different measures are used to evaluate the forecasting accuracy of the models. Also two methods of estimation the parameters of GARCH models are compared for forecasting oil price volatility. The results suggest that in our study, maximum likelihood estimation (MLE) gives better results for estimation than generalized method of moments (GMM)

Wybór strategii określania warunku opłacalności wydobycia w kopalniach odkrywkowych w warunkach niepewności cen w oparciu o wielokryterialny system rankingowy z uwzględnieniem czynników ryzyka

Cut-off Grade Strategy (COGS) is a concept that directly influences the financial, technical, economic, environmental, and legal issues in relation to exploitation of a mineral resource. A decision making system is proposed to select the best technically feasible COGS under price uncertainty. In the proposed system both the conventional discounted cash flow and modern simulation based real option valuations are used to evaluate the alternative strategies. Then the conventional expected value criterion and a multiple criteria ranking system were used to rank the strategies based on the two valuation methods. In the multiple criteria ranking system besides the expected value other stochastic orders expressing abilities of strategies in producing extra profits, minimizing losses and achieving the predefined goals of the exploitation strategy are considered. Finally, the best strategy is selected based on the overall average rank of strategies through all ranking systems. The proposed system was examined using the data of Sungun Copper Mine. To assess the merits of the alternatives better, ranking process was done at both high (prevailing economic condition) and low price conditions. Ranking results revealed that at different price conditions and valuation methods, different results would be obtained. It is concluded that these differences are due to the different behavior of the embedded option to close the mine early, which is more likely to be exercised under low price condition rather than high price condition. The proposed system would enhance the quality of decision making process by providing a more informative and certain platform for project evaluation.Strategia doboru granicy opłacalności (COGS) jest koncepcją mająca bezpośredni wpływ na kwestie finansowe, techniczne, ekonomiczne, środowiskowe oraz prawne związane z eksploatacją surowców naturalnych. Zaproponowano system decyzyjny umożliwiający wybór najkorzystniejszej fizycznie wykonalnej strategii doboru opłacalności wydobycia w warunkach niepewności cen. W proponowanym systemie do analizy alternatywnych strategii wykorzystuje się konwencjonalne metody oparte o analizy przepływu strumienia gotówki oraz nowoczesne techniki symulacji rzeczywistych opcji. Następnie zastosowano tradycyjny system oparty o kryterium wartości oczekiwanej oraz system rankingu wielokryterialnego do określenia rankingu strategii, w oparciu o dwie metody oceny. W systemie wielokryterialnym obok wartości oczekiwanej uwzględnia się inne dane stochastyczne określające zdolność poszczególnych strategii do generowania dodatkowych zysków, do ograniczania strat i osiągania wcześniej zdefiniowanych celów. W etapie końcowym dokonuje się wyboru optymalnej strategii w oparciu o całkowity ranking strategii uwzględnionych w systemie. Proponowane podejście testowano w oparciu o dane uzyskane z kopalni miedzi Sungun. Aby ocenić zalety najlepszej alternatywy, ranking przeprowadzono przyjmując warunki wysokich i niskich cen. Wyniki rankingu wykazały, że w warunkach różnych cen i przy zastosowaniu różnych metod oceny, uzyskane rezultaty będą się różnić. Należy wnioskować, że różnice te spowodowane są różnicami w podejściu do wbudowanej opcji wczesnego zamknięcia kopalni, która ma większą szansę na realizację w warunkach niskich cen, a nie wysokich. Proponowany system podniesie jakość procesu decyzyjnego poprzez dostarczenie platformy dodatkowych informacji dla oceny przedsięwzięcia