Once a model of a financial system has been constructed, we can simulate the system to predict how it will perform through time. By definition, however, the performance of such a system is stochastic (i.e., inherently variable). That is, we can't say exactly how an investment will perform; we can only use historical data to describe the behavior statistically (e.g., based on a mean growth and a volatility).
In addition to this inherent variability, we might also be uncertain about some of the input parameters controlling the model. For example, if a security was new, we would not have a good statistical basis for defining controlling factors like mean growth and volatility. In this case, we could enter these as probability distributions in order to capture this uncertainty.
Variability and uncertainty are represented in GoldSim using Monte Carlo simulation. Monte Carlo simulation consists of calculating a large number of “realizations” (potential futures):
In this example 100 equally probable realizations of potential future behavior of an investment are plotted. These realizations were generated using Monte Carlo simulation.
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