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I have 2 stocks in my portfolio A and B.A is currently at 50 dollars and B at 40 dollars. Correlation between A and B is 0. Let us say I bought the stocks today at 50 and 40 dollars. If I wish to use a Monte Carlo simulation to estimate the individual stock prices of my portfolio and the variance at the end of 1 year, what other info do I need?

If the mean of the stock A was 50, that of B was 40 , the SD of A was 5, that of B was 4(all measured over last 5 years), does that give me enough info to proceed? Do I just draw random prices from 2 log normal distributions(meanA=50,meanB=40,sigmaA=5,sigmaB=4) ,take the average of the prices and call it done?

What else do I need to consider for a basic simulation? This is for education, not profit.

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  • $\begingroup$ From the information given in your question I guess you want to do a stochastic Monte Carlo simulation using a geometric brownian motion for each? Or are you planning to perform a bootstrap Monte Carlo simulation for each? Both is possible on a stand alone basis since the stocks are uncorrelated. Latter one is „easier“ and depending on the set up less costly. The first one would be definitely more than just picking values from log normal distributions. $\endgroup$ – Fokko Mar 27 at 5:49
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Here is an excellent example of a code walkthrough of a Brownian Motion Monte Carlo Simulation. (Even if you're not coding this in Python - its just really nicely spelled out here step by step.)

In the article you will see that in addition to Mean and Standard Deviation you will also need Variance in order to calculate Drift.

Also, there are some other considerations in the article as to how to actually set up the Monte Carlo Simulation.

Hope this helps!

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