[R] Calculating confidence intervals on subset of bootstrap sample statistics

[Abiel X Reinhart]

I have a vector of monthly log asset price returns. I would like use the boot package to sample one-year returns, and then calculate confidence intervals on the loss distribution only. More concretely, I would like to say something like "99% of LOSSES (not RETURNS) are above cutoff X."

If the variable "x" contains my returns and I was interested in confidence intervals for the entire distribution of returns, I might do something like this:

results <- boot(x, statistic=function(d, indx) sum(d[indx[1:12]]), R=100000)
boot.ci(results, conf=.98)

In this case I establish a cutoff for the bottom 1% of the return distribution, but this is not the same thing as the bottom 1% for the losses only. It seems one thing to do would be to just extract the sample returns from the results object, filter them, and then calculate confidence intervals manually. However, I was curious if there was a simple way to subset the results object so that it could be fed directly into boot.ci()

Thanks.

Abiel
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