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I have several assets, each with different return histories.

Some of the assets have 75 days of return history, others have 40 or so days. In calculating a robust covariance matrix, should I be using a resampling with replacement technique or should I be using an expectation maximization algorithm?

I was thinking of truncating the samples so that they all share ~40 days of return history, and then resampling that distribution of returns with replacement.

But, I'm not sure that's ideal.

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What is your primary concern: robustness (w.r.t. outliers) or missing data? Neither resampling or EM do adress the former. There are however ways to deal with both, are you interested? –  Quartz Sep 2 '13 at 15:47
    
I'm concerned with both, but my primary concern is with the robustness of the covariance matrix. I actually thought resampling would address the robustness, but not the missing data. I'm thinking the two are mutually exclusive techniques, each addressing a different component. –  tragen907 Sep 2 '13 at 19:40
    
Whops sure, sloppy speed writing on my part, of course I just wanted to say that neither method adresses both aspects and that they are not mutually exclusive, and having different targets they can't be compared. There's a vast literature nowadays (even just for variants of EM), no need to stick to (rough and outdated) resampling. My favourite combined method is: ecares.org/ecaresdocuments/seminars1011/frahm.pdf . However it's not extensible to asynchronous quotes. –  Quartz Sep 3 '13 at 9:50
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