The Monte Carlo simulation method relies on neither a normal distribution nor past returns and, as a result, is able to accommodate bonds that may contain embedded options.
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Monte Carlo simulation assigns random weights to each observations, you can also take that weight as probability of occurrence.
So this randomness eliminates the reliability on any existing pattern or value in the data. So the curve may not necessarily be a bell shaped normal distribution curve when plotted. And when you run this exercise say 10000 times, the same data point takes 10000 different weights. So if your portfolio earned 200% on one particular day… It might skew your returns on data chart individually but not when it is taking a wt. Of .005 (say).
Hence the above statement.
But how can it accommodate bonds with embedded options?
Thik of a possibility when the bond is called or put by the investor… Then you assign 10000 different weights to the possibility… Then you pull down the payoff each time and do some random amalgamation say avg. So the event of exercise of option is given different weights… Normal distribution would face a kink situation ( like say a negative spike ) and won’t stay Normal under such situation.
Way too complicated but got the crux , thank you .