[R-SIG-Finance] PCA in Risk Control with R

[Brian G. Peterson]

Why don't you disguise a subset of your data and provide a working example?

Both you and the list will get more out of it if we can all work on something 
that is actually executable in R, per the posting guide.

Your problem is interesting and relevant, so put a little more effort into it, 
and I'm sure you'll get collaborators in working through it.

Regards,

   - Brian

Benji Famel wrote:
> Hello,
> 
> my apologies if I do something wrong - first posting for me.
> 
> I am trying to apply PCA on the daily history of a bunch of forward
> curves and run into my depths of ignorance.  I would appreciate some
> help...
> 
> My aim is to use PCA for risk control.  I.e. estimate the
> eigenverctors and eigenvalues and build the principal components at
> some confidence level, e.g. 95%.  If, for example, we were looking at
> the first 3 components only, I would
> - estimate PC1up, PC1dn, PC2up, PC2dn, PC3up and PC3dn.
> 
> Let's assume that
> - PC1up is worse for my position than PC1dn,
> - PC2up is worse than PC2dn and
> - PC3dn is worse than PC3up
> I would then 'add' these worse for me components (PC1up, PC2up and
> PC3dn) and run my position through them to get a measure of risk at
> that confidence level.
> 
> To do the PCA, I first foundthe log returns, let's call them Returns.
> I then do:
> pcdat <-princomp(Returns, cor=TRUE)
> and calculate the principal components like this (this is where I am
> very foggy...):
> 
> PC <- exp(someQuantile*t(pcdat[[2]])*sqrt(pcdat[[1]])*sd(Returns))   #
> somQuantile = 1.64 for a 95% CL
> 
> 
> As much as I looked around, people discuss the benefits of PC but not
> how to recombine the principal components at some confidence interval
> to get a shocked curve.
> 
> Could anyone help?
> 
> Thank you,
> Benji
> 
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-- 
Brian G. Peterson
http://braverock.com/brian/
Ph: 773-459-4973
IM: bgpbraverock