I have used ifelse in count variables to count the number of times in a
simulation the values of a vector of logprice fall within mutually exclusive
ranges. However, there is a double count in the result i.e. i am getting output
indicating values falling in mutually exclusive ranges. Here is the code and
result
R script
niter = 1e5 # number of iterations is 10^5
CountLoss = rep(0,niter)
CountProf = rep (0,niter)
set.seed(2009) # enables reproducibility of result if script run again"
for (i in 1:niter)
{
r = rnorm(100,mean=.05/253,
sd=.23/sqrt(253)) # generate 100 random normal numbers
logPrice = log(1e6) + cumsum(r) #vector of 100 days log prices
maxlogP = max(logPrice) # max price over next 100 days
minlogP = min(logPrice)
CountLoss[i] <- ifelse (minlogP < log(950000), 1, ifelse (maxlogP > log
(1000000), 0, 1))
CountProf[i] <- ifelse (maxlogP < log (1100000),0,1)
}
sum(CountLoss)
mean(CountLoss) # fraction of times out of niter that stock is sold for a loss
in a 100 day period
sum(CountProf)
mean(CountProf) # fraction of times out of niter that stock is sold for a
profit in a 100 day period
Output
sum(CountLoss)
[1] 64246
> mean(CountLoss) # fraction of times out of niter that stock is sold for a
> loss in a 100 day period
[1] 0.64246
> sum(CountProf)
[1] 51857
> mean(CountProf) # fraction of times out of niter that stock is sold for a
> profit in a 100 day period
[1] 0.51857
CountLoss and CountProf should sum to less than the number of interations. When
I troubleshoot by reducing the number of iterations and that size of the
logprice, I can't reproduce the contradicion.
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