[tech-spec] Monte Carlo
- From: James Sogi <jsogi@xxxxxxxxxxxxx>
- To: tech-spec@xxxxxxxxxxxxx
- Date: Sun, 29 May 2005 14:10:37 -1000
The Professor, Chair, and the master simulator use Monte Carlo
techniques in recent posts on http://www.dailyspeculations.com/ to
determine whether various moves are consistent with randomness or not.
Will ?permute(gtools) from a wiz favorite package gregmisc ' Randomly
Permute the Elements of a Vector' do the trick for the R users?
The procedure for a Monte Carlo test was described in dailyspec:
"In order to address the question posed, we found the percent change
over each month, and we stored it in a list. We then simulated the S&P
time series by starting at the same initial value and moving to the next
month's value by randomly selecting one of the percent changes in our
list and using that as the current month's percent change. We sample
without replacement, i.e., we use all of the same values but in a random
order. We perform our algorithm on each of the simulated time series and
keep track of the data. Twenty trials were performed.
Do we find a significant distinction between the actual S&P data and the
What is the test in R to determine significant difference between
actual and random, or is it as Chair summarizes, "For each simulation,
there was an average rally duration. Taking the average of these yields
15 months, with 25 rallies on average. The standard deviation is 3.4
months. The actual value for the average rally duration is about 4
standard deviations away from the mean, a significant finding. This
indicates that actual rallies tend to last longer than in the
simulations." Is it as simple as computing the standard deviation from
the mean of the random samples and not requiring a table or test?
I would appreciate your comments and corrections.
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