Hello Mr. Bernake…

December 10, 2011
By

(This article was first published on Adventures in Statistical Computing, and kindly contributed to R-bloggers)

I was playing around tonight and came across something that looked odd.  Using the importSeries() created before, I grabbed dividend adjusted returns for SHY, IEF, and TLT (iShares Short, Medium, and Long Maturity Treasury ETFs respectively).  I was looking at efficient frontiers (more on that later) and saw that the IEF was very dominant.

options(scipen=100)
options(digits=4)

from = “2001-01-01”
to = “2011-12-09”

tlt = importSeries(“tlt”,from,to)
shy = importSeries(“shy”,from,to)
ief = importSeries(“ief”,from,to)

merged = merge(tlt,shy)
merged = merge(merged,ief)

vars = c(“tlt.Return”,”shy.Return”,”ief.Return”)

table.AnnualizedReturns(merged[,vars],Rf=mean(merged[,”shy.Return”],na.rm=TRUE))

Results:

> table.AnnualizedReturns(merged[,vars],Rf=mean(merged[,”shy.Return”],na.rm=TRUE))

tlt.Return shy.Return ief.Return
Annualized Return 0.0721 0.0284 0.0628
Annualized Std Dev 0.1403 0.0173 0.0740
Annualized Sharpe (Rf=2.81%) 0.3018 -0.0087 0.4497

It struck me as odd that the IEF was so dominant with an annualized return of about 1% below TLT, but half the risk giving it a 50% increase in the Sharpe Ratio.  Then it hit me.  “Oh yeah, the QE programs were aimed at the middle of the yield curve.”

Note to self, next time QE rolls around, play the IEF…

To leave a comment for the author, please follow the link and comment on their blog: Adventures in Statistical Computing.

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