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You are right, the mean is going to be $S_0 e^{\mu t}$. You may want to increase the number of simulations by the way, 1,000 ain’t that many. Since you know $(S_t)$ analytically in closed form, simulating and averaging does not really provide you with any further information. We know all moments of $(S_t)$ in closed-form anyway and can compute probabilities ...


Let's try a simple approach, ignoring the difference between sample and population variance, and assuming the process is just the standard brownian - with no drift and sigma term. Generalisation should be easy. We define a process Y as equal to standard brownian, but we are assuming finite sampling with difference between two observations equal to $\Delta t$...


The code below is part of a VBA project I did to calculate VaR with Monte Carlo returns. If you eliminate the -1 at the end all values are positive. You just need to add your own risk free and standard deviation. Excel RAND() is same as VBA RND(). For i = 1 To 10000 stockReturn(i) = Exp((RiskFree - 0.5 * StDv ^ 2) + StDv * Application.NormInv(Rnd()...


You need for Returns = normsinv(rand()) * Sigma + Mu for each period. Then Price = e(Price-1 + Return) for the Monte Carlo price enjoy ;-)

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