Long-term mine plans are based on forecast future metal prices. By the time the development is put in place, the forecasts may have been proved wrong and the production plan might not meet the company's financial objectives. At that point, the common reaction to this situation is to create a new revised long-term plan and spend more capital, only to find out at a later time that the metal prices have changed again. This results in an inefficient use of capital with low returns to the investors. The objective of this thesis is to develop a methodology to determine the cut-off grade and production rate of a narrow-vein underground mine such that the long-term strategic plan is robust. As a requirement to do so, it is necessary to have a good understanding of the resources, revenues, capital and operating costs as a function of the design parameters. Also, the operational limits of the mine must be determined so that the solution is practical. Afterwards, annual metal prices are randomly generated with a Monte Carlo process on stochastic metal price models, and the combination of production rate and cut-off grade yielding the highest net present value is identified and recorded. This process is repeated many times, and the probabilities of the solutions occurring at any given design combination are calculated. The results are plotted on a bubble graph, where the size of a bubble is directly proportional to the probability a solution occurs at that point. Finally, the combination with the largest bubble is the solution, as this point has the highest probability of yielding the highest net present value in most circumstances. The model was first tested on an actual gold-copper orebody where very detailed resource and cost information was available. The methodology was applied with success and the solution reflected the important impact of the copper milling and roasting process on revenues. Other tests were then done on a hypothetical gold orebody and the results showed a great degree of sensitivity to the average grade of the deposit. / Thesis (Ph.D, Mining Engineering) -- Queen's University, 2008-04-25 12:42:24.623

In the first part of this thesis the prices of six non-ferrous metals (aluminum, copper, lead, nickel, tin, and zinc) are used to assess the forecasting performance of GARCH models. We find that the forecasting performances of GARCH, EGARCH, and TGARCH models are similar. However, we suggest the use of the GARCH model because it is more parsimonious and has a slightly better statistical performance than the other two. In the second part, the prices of six non-ferrous metals and the price of crude oil are used to examine the dynamic links between oil and metal returns by using the BEKK specification of the multivariate GARCH model and the Granger causality-in-variance tests. Results of our study agree with the previous studies in that the crude oil market volatility leads all non-ferrous metal markets. In order to move as far away from the effects of 9/11, daily data for the period December 12, 2003 &ndash / December 15, 2008 is used for the data analysis part of the thesis.

In this thesis the relationship between Brent oil prices, LME copper prices, Turkish gold and silver spot prices, XU100 index, interest rate and exchange rate is examined. Their long run Granger causality relationship is investigated by looking at Wald statistics. The short run relationship between them is examined by using generalized impulse responses. The data range is from January 2, 2002 to February 24, 2011. Due to the oil crisis in 2008, we divide the data into three periods: January 2, 2002 to December 31 as first period, 2007, from January 1, 2008 to December 31 as second period, 2008 and January 1, 2009 and February 24, 2011 as third period. We conduct each test separately for these periods but in third period we use Toda-Yamamoto procedure since maximum order of integration is 1.