When a system is too complex to be analyzed using ordinary methods, investment analysts frequently use Monte Carlo simulation. Monte Carlo simulation involves trying to simulate the conditions that apply to a specific problem by generating a large number of random samples using a random number generator on a computer.
After generating the data, quantities such as the mean and variance of the generated numbers can be used as estimates of the unknown parameters of the population (parameters are too complex to find through normal methods).
The term "Monte Carlo simulation" derives from the generation of a large number of random samples, such as might occur in the Monte Carlo Casino.
Limitations of Monte Carlo Simulation:
Historical simulation samples from a historical record of returns (or other underlying variables) are used to simulate a process because the historical record provides the most direct evidence on distributions (and that past applies to the future). In contrast, Monte Carlo simulation uses a random number generator with a specified distribution. A drawback is that any risk not represented in the time period selected will not be reflected in the simulation. For example, if a stock market crash did not take place in the sample period, such a risk will not be reflected in the simulation. In addition, this method does not lend itself to "what-if" analysis.
| sahilb7: Monte Carlo: Large number of random samples|
Historical Simulation: Historical record of returns
|ashish100: Damn should've just scrolled down and read sahilb7's comment instead.|