![]() ![]() In: 5th Annual International Conference on Real Options – Theory Meets Practice, UCLA, Los Angeles, USA, July 13-14 (2001) 23 (July 2000)ĭias, M.A.G.: Investment in Information for Oil Field Development Using Evolutionary Approach with Monte Carlo Simulation. of Electrical Engineering, PUC-Rio, Brazil, p. Springer, Heidelberg (1996)īatista, F.R.S.: Avaliação de Opções de Investimento em Projetos de Exploração e Produção de Petróleo por Meio da Fronteira de Exercício Ótimo da Opção, Master’s Dissertation, Department of Industrial Engineering, PUC-RIO (2002) (in portuguese)ĭias, M.A.G.: Real Option Evaluation: Optimization under Uncertainty with Genetic Algorithms and Monte Carlo Simulation, Working paper, Dep. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution programs. Journal of the American Statistical Association 88, 1392–1397 (1993) Tang, B.: Orthogonal Array-based Latin Hypercube. Owen, A.B.: Orthogonal Arrays for Computer Experiments, Integration and Visualization. ![]() McKay, M.D., Conover, W.J., Beckman, R.J.: A Comparison of Three Methods for Selecting Values of Input Variables in Analysis of Output from a Computer Code. (eds.) Proceedings of the Winter Simulation Conference (1997) In: Andradóttir, S., Healy, K.J., Withers, D.H., Nelson, B.L. Saliby, E.: Descriptive Sampling: an Improvement Over Latin Hypercube Sampling. Fuzzy Sets and Systems 122, 315–326 (2001)ĭong, W.M., Wong, F.S.: Fuzzy Weighted Averages and Implementation of the Extension Principle. Fuzzy Sets and Systems 107, 335–348 (1999)Ĭarlsson, Christer, Fullér, Robert: On Possibilistic Mean Value and Variance of Fuzzy Numbers. Doctoral Thesis, Department of Electrical Engineering of the Pontifical Catholic University of Rio de Janeiro - PUC-Rio (2004) (in portuguese) Lazo, J.G.L.: Determinação do Valor de Opções Reais por Simulação Monte Carlo com Aproximação por Números Fuzzy e Algoritmos Genéticos, Ph.D. Grant, D., Vora, G., Weeks, D.E.: Path-Dependent Options: Extending the Monte Carlo Simulation Approach. Princeton University Press, Princeton (1994) ACM Transactions on Modeling and Computer Simulation 7, 447–477 (1997)ĭixit, A.K., Pindyck, R.S.: Investment under Uncertainty. Statistics and Probability Letters 31, 275–279 (1997)ĭevroye, L.: Random Variate Generation for Multivariate Unimodal Densities. Society for Industrial & Applied Mathematics (1992)ĭevroye, L.: Simulating Theta Random Variates. Niederreiter, H.: Random Number Generation and Quasi-Monte Carlo Methods. In: CBMS-NSF Regional Conference Series in Applied Mathematics, vol. (63), SIAM, Philadelphia (1992) Niederreiter, H.: Random Number Generation and Quasi Monte Carlo Methods. In: Proceedings of the 3rd International Seminar on Digital Image Processing in Medicine, pp. Niederreiter, H.: Quasirandom Sampling Computer Graphics. Morokoff, W.J., Caflish, R.E.: Quasi-Monte Carlo Integration. In: Proceedings of the 1992 Winter Simulation Conference, pp. L’Ecuyer, P.: Testing Random Number Generators. L’Ecuyer, P.: Uniform Random Number Generation. L’Ecuyer, P.: Efficient and Portable Combined Random Number Generators. L’Ecuyer, P.: Random Numbers for Simulation. Marsaglia, G., Maclaren, M.D., Bray, T.A.: A Fast Procedure for Generating Normal Random Variables. This process is experimental and the keywords may be updated as the learning algorithm improves. These keywords were added by machine and not by the authors. This method makes use of a genetic algorithm and of known stochastic processes for representing market uncertainty (commodity prices), which are used in combination with stochastic simulations (Monte Carlo simulation) and with variance reduction techniques. The second part describes the method for approximating an optimal decision rule and determining the value of a real option for the case where there are several project investment alternatives (options). The first part describes the method which approximates the value of a real option using fuzzy numbers to represent technical uncertainties and known stochastic processes to represent market uncertainty (commodity prices), which are used in combination with stochastic simulations (Monte Carlo simulation) so as to reduce the computational time spent on Monte Carlo simulation runs. This chapter describes, in two parts, the methodology proposed for obtaining an approximation of the real option value and of the optimal decision rule for several project investment options by considering technical and market uncertainty. ![]()
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