When a system is too complex to be analyzed using ordinary methods, investment analysts frequently use

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.

- It allows us to experiment with a proposed policy and assess the risks before actually implementing it. For example, it is used to simulate the interaction of pension assets and the liabilities of defined benefit pension plans.
- It is widely used to develop estimates of
**Value at Risk**(VAR). VAR involves estimating the probability that portfolio losses exceed a predefined level. - It is used to value complex securities such as European options, mortgage-backed securities with complex embedded options.
- Researchers use it to test their models and tools.

Limitations of Monte Carlo Simulation:

- It is a complement to analytical methods. It provides only statistical estimates, not exact results.
- It does not directly provide precise insights as analytical methods do. For example, it cannot reveal cause-and-effect relationships.

sahilb7: Monte Carlo: Large number of random samplesHistorical Simulation: Historical record of returns |

ashish100: Damn should've just scrolled down and read sahilb7's comment instead. |