TY - CHAP
T1 - Real option value calculation by Monte Carlo simulation and approximation by fuzzy numbers and genetic algorithms
AU - Lazo, Juan Guillermo Lazo
AU - Dias, Marco Antonio G.
AU - Pacheco, Marco Aurélio Cavalcanti
AU - Vellasco, Marley Maria Bernardes Rebuzzi
N1 - Copyright:
Copyright 2009 Elsevier B.V., All rights reserved.
PY - 2009
Y1 - 2009
N2 - 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. 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. 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). 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.
AB - 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. 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. 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). 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.
UR - http://www.scopus.com/inward/record.url?scp=59549084917&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-93000-6_5
DO - 10.1007/978-3-540-93000-6_5
M3 - Capítulo de libro
AN - SCOPUS:59549084917
SN - 9783540929994
T3 - Studies in Computational Intelligence
SP - 139
EP - 186
BT - Intelligent Systems in Oil Field Development under Uncertainty
A2 - Pacheco, Marco A.C.
A2 - Vellasco, Marley B.R.
PB - Springer Berlin
CY - Heidelberg
ER -