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Monte Carlo simulation has enjoyed a resurgence in financial literature in recent years.This paper explores the reasons why implementing Monte Carlo simulation is very difficult at best and can lead to incorrect decisions at worst.The problem is that the typical assumption set used in Monte Carlo simulation assumes normal distributions and correlation coefficients of zero, neither of which are typical in the world of financial markets. It is important for planners to realize that these assumptions can lead to problems with their analysis. Financial planners will find that exploratory simulation provides equivalent or better answers and is simpler to implement without assumptions.
"Those who cannot remember the past are condemned to repeat it."
George Santayana (1863-1952)
It was a wonderful time. Everybody had brand-new computers that were more powerful than anything that had come before. With the increase in computational power came the new toys with which to play. Linear programming, nonlinear programming, integer programming, goal programming, queuing theory, Box-Jenkins ARIMA models, Almon distributed lags and Monte Carlo simulation were among the many toys under the tree. However, just like the day after Christmas, some of the toys still worked, some were ignored and some were broken. And the parents learned which toys not to buy in the future.
Unfortunately, the parents turn into grandparents with foggy memories, and the children into parents who don't remember the toys that broke. So several generations later, the old toys are back under the tree waiting to break again.
If this Christmas story sounds like the 1980s and 1990s with the microcomputer, think again. The 1980s and 1990s are just history repeating itself. It is actually the story of the universities in the mid 1960s, who were taking delivery of the first mainframe computers that had the computational power to do something useful. These multi-million dollar machines were not under the tree for most businesses, let alone for a small child. Only the universities with their research and instructional funds could afford them. It was here that all of the quantitative toys were played with and tested-including Monte Carlo simulation, which is the topic for this paper.
It was 1964 when Hertz (1964] first suggested using Monte Carlo simulation in business applications. This paper created an explosion...