# Principal component analysis to yield curve change

December 19, 2010
By

(This article was first published on My Life as a Mock Quant in English, and kindly contributed to R-bloggers)

In quantitive finance,it is often said that yield curve change is explained by three factor,
parallel shift”, “twist” and “butterfly”.
Because I found that we can get historical yield curve data from FRB’s web site, I check whether these proverbial facts are correct or not.Yield curve data can be downloaded to click “Go to download” and “Download File” button. Default data format is csv. If you would like to get data another format, you should click “Build package” button to change format.

I assume that downloaded data is located at  “C:\tmp”

`#load dataterm.structure <- read.csv("C:\\tmp\\FRB_H15.csv",stringsAsFactors=FALSE)#use nearest 1000days data only.term.structure <- tail(term.structure,1000)#First column is "DATE".I don't need it.term.structure <- term.structure[,-1]label.term <- c("1M","3M","6M","1Y","2Y","3Y","5Y","7Y","10Y","20Y","30Y")colnames(term.structure) <- label.term#some rows have invalid value. I erase that's rowterm.structure　<- subset(term.structure,term.structure\$'1M' != "ND")term.structure <- apply(term.structure,2,as.numeric)#calculate diff.term.structure.diff <- diff(term.structure)`

Now,I have gotten yield curve change data.

Next,do principal component analysis and plot the result.

`term.structure.princomp<- princomp(term.structure.diff)factor.loadings <- term.structure.princomp\$loadings[,1:3]legend.loadings <- c("First principal component","Second principal component","Third principal component")par(xaxt="n")matplot(factor.loadings,type="l",  lwd=3,lty=1,xlab = "Term", ylab = "Factor loadings")legend(4,max(factor.loadings),legend=legend.loadings,col=1:3,lty=1,lwd=3)par(xaxt="s")axis(1,1:length(label.term),label.term)`

Result image is shown like below

These result imply that Each three principal component correspond to parallel shift”, “twist” and “butterfly”.

Cumulative Proportion are shown by “summary” function.
`> summary(term.structure.princomp)Importance of components:                          Comp.1    Comp.2     Comp.3     Comp.4     Comp.5      Comp.6      Comp.7      Comp.8Standard deviation     0.2028719 0.1381839 0.06938957 0.05234510 0.03430404 0.022611518 0.016081738 0.013068448Proportion of Variance 0.5862010 0.2719681 0.06857903 0.03902608 0.01676075 0.007282195 0.003683570 0.002432489Cumulative Proportion  0.5862010 0.8581690 0.92674803 0.96577411 0.98253486 0.989817052 0.993500621 0.995933111`

As a result, yield cuve change can be explained by three principal component.

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