文档介绍：Real Options Evaluation: Optimization under Uncertainty
with ic Algorithms and Monte Carlo Simulation
Revision data: September 20, 2000
Abstract:
Complex real options models for project economics evaluation suffer the curse of
dimensionality, with several sources of uncertainties and with several options to invest in
information. Details from the changing practical reality highlight the curse of modeling
problem. Monte Carlo simulation is considered a good way to face these problems, but
there is the difficult problem to optimize. This paper presents a model of optimization
under uncertainty with ic algorithms and Monte Carlo simulation. This approach
permits to get new insights for the real options theory. Using the Excel-based software
RiskOptimizer for a simple case (with a known value) and for a plex real
options model with investment in information. Some results from several experiments are
presented with improvement suggestions. The strengths and weaknesses of
RiskOptimizer are pointed out.
Keywords: real options, ic algorithms, Monte Carlo simulation, optimization under
uncertainty, economic valuation of projects.
1 - Introduction
The practical problems of the curse of dimensionality1 and the curse of modeling2 have
directed some recent real options research3 to the Monte Carlo simulation approach, due
its modeling flexibility. The major problem is the difficulty to perform optimization
(backward) with simulation (forward), which in general is necessary for American-type4
options. Among the papers that use new optimization techniques are Cortazar &
Schwartz (1998), Longstaff & Schwartz (1998), Broadie & Glasserman & Jain (1997),
and Ibáñez & Zapatero (1999).
This paper presents another possibility to optimize a Monte Carlo simulation of real
options problems: the use of the puting approach. Specifically are used
ic algorithms (GA) as optimizer tool. The optimization problem is not more a
Bell