Explain the inputs and choices a simulation involves and interpret what it produces

A simulation requires several decisions, starting with what to simulate, such as asset, factor or strategy returns. Next come the distribution and its parameters, including means, volatilities, correlations and whether to allow fat tails or regime shifts. Then come the number of trials and the horizon. The output is a distribution summarised by its mean, percentiles, VaR, conditional VaR and the probability of meeting or missing targets. A normal distribution usually understates tail losses compared with a fat-tailed distribution fitted to the same data, so conditional VaR can differ far more than the mean. The trap is interpreting the simulated mean as a forecast while ignoring the spread and tail percentiles, which are the reason for running a simulation.