This is an interesting article about calibrating your capital market assumptions (CMAs) in your Monte Carlo simulations:
From the article: The goal is to understanding how your Monte Carlo results compare to the real-world behavior of a financial plan during a period of uncertainty.
...(adjusting) CMAs is helpful to run Monte Carlo analysis with the assumptions they feel most comfortable with. Understanding how CMA data is created is an important step in interpreting and trusting your Monte Carlo results. Getting a Monte Carlo result using your CMA data is an appropriate starting point to understanding uncertainties. You can then change your CMAs to better represent the market environment and get an alternate Monte Carlo score.
Monte Carlo scores based on revised CMAs are an effective way to test your client’s financial resiliency if the macroeconomic environment changes. You can see the impact of changes like higher inflation or increased volatility while testing different recommendations. When Monte Carlo scores don’t differ significantly, you can assure clients that their financial plans are resilient, and that market uncertainty doesn’t translate to drastic changes in their plan.
@smatthews51 not sure you want to give PRC users the ability to adjust starting parameters in Monte Carlo simulations but might be worth thinking about 🤔.
