In this article I thought I would take a look at and compare the concepts of “Monte Carlo analysis” and “Bootstrapping” in relation to simulating returns series and generating corresponding confidence intervals as to a portfolio’s potential risks and rewards.

Both methods are used to generate simulated price paths for a given asset, or portfolio of assets but they use slightly differing methods, which can appear reasonably subtle to those who haven’t come across them before. Technically Bootstrapping is a special case of the Monte Carlo simulation, hence why it may seem a little confusing at first glance.

With Monte Carlo analysis (and here we are talking specifically about the “Parametric” Monte Carlo approach) the idea is to generate data based upon some underlying model characteristics. So for example, we generate data based upon a Normal distribution, specifying our desired inputs to the model, in this case being the mean and the standard deviation. Where do we get these input figures from I hear you ask…well more often than not people tend to use values based on the historic, realised values for the assets in question.