Monte Carlo Simulation and Historical Simulation

Simulation
Problem solving technique used to approximate the probability of certain outcomes by running multiple trial runs, using random variables.

Monte Carlo simulation
Involve the use of a computer and models to find approximate solutions for complex problems.
In finance, Monte Carlo simulation involves in identifying risk factors associated with the problem and specifying probability distributions for them. Repeated random sampling from these distributions is then used to simulate the risk factors.

Limitation

• Difficult and costly to implement

Historical simulation
Involve the use of a repeated sampling from historical data series to establish the behavior of important risk factors in generating returns.

Limitation

• Only reflect the risks that exist in the historical series being used and therefore it is difficult to use it for “what-if” experiments or to investigate the impact of “rare” events.