What is Monte Carlo Simulation?
Monte Carlo simulation is a quantitative technique that estimates the probability distribution of an outcome by running thousands of trials with randomly drawn input values. It is used to price complex derivatives, evaluate project risk, model insurance losses, and stress-test portfolios.
How It Works
- Pick a probability distribution for each uncertain input
- Draw a random value from each distribution and compute the outcome
- Repeat thousands or millions of times to build the output distribution
- Read off the mean, percentiles, value at risk, or probability of loss
- Computationally intensive but well-suited to path-dependent and non-normal problems
Saudi Context
Saudi banks and insurance companies use Monte Carlo simulation for VaR, capital adequacy, and reserving calculations under SAMA and Insurance Authority frameworks. Mega-project investment committees also use it to test sensitivity of project NPVs to a basket of correlated risks (oil price, demand, capex overrun).
Example
A Saudi infrastructure investor runs 10,000 simulations of a toll-road concession with random draws for traffic growth, inflation, and operating cost. The output distribution shows a 90% probability the IRR sits between 7% and 12%, with a 5% chance of falling below the 6% hurdle. The investment committee uses these tail numbers to size the equity buffer.