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Great to see you posting again CV. From what I can see, the distribution problem can also be handled in excel itself by multiplying the standard deviation by a constant value, for a given mean (in a log distribution). If the mean daily return is found out to be .0% a day with a SD of 1 (say), a constant of 1.2 (say) is multiplied to get a value of 1.2% ( instead of 1, as assumed in lognormal distribution).
The function, NormalRandom(0,1.2) [where 0 is the mean and 1.2 is the new standard deviation] has the usual 99% of the items within 3.6 of the original standard deviation and not 3.
Once the returns for n days are calculated and the 1st price is known, we can find the stock price for each of the n days by Previous price*(1 + current returns).
Of course, while this deals with kurtosis, it doesn't take into consideration the skewness. Any other way to handle this problem CV?
The function, NormalRandom(0,1.2) [where 0 is the mean and 1.2 is the new standard deviation] has the usual 99% of the items within 3.6 of the original standard deviation and not 3.
Once the returns for n days are calculated and the 1st price is known, we can find the stock price for each of the n days by Previous price*(1 + current returns).
Of course, while this deals with kurtosis, it doesn't take into consideration the skewness. Any other way to handle this problem CV?