Quant - Monte Carlo
one of the major limitations of monte carlo simulation is that it:
1) cannot provide the insight that analytic methods can
2) does not lend itself to performing what if scenarios
3) required that variables be modeled using the normal distribution.
I guessed #2 but the correct answer was #1. The rationale is that MC sims are fairly complex and will provide answers that are no better than the assumptions used and that it cannot provide insights that analytic methods can. I would say that is completely false. I believe that utilizing simulation rather than an OPM or black-scholes is much better, especially with you are dealing at the ends of the spectrum in terms of moneyness of options as BS models break down due to illiquidity.
#2 is, in my opinion, more accurate. As an example, you can run a very easy “what-if” aka sensitivity table in excel for a DCF and sensitize the growth rate and discount rate to determine the PV of CF’s. However, if you wanted to sensitize something in the monte-carlo, you would have to re-run a simulation, and depending on complexity, could take hours to run a single simulation. Any thoughts?