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2

If you look at changes of the points on the yield curve, then you probably find something stationary - right? Applying PCA on the covariance of these changes makes sense. E.g. you will find out that on PC describes a parallel shift (a change in the yield curve). Look at this question too: What do eigenvalues/eigenvectors of the yield/forward rates ...

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Following @silencer's comment, your formula for variance is wrong. I would suggest that instead of trying to re-invent the wheel, you just use the formula that everyone else uses. So I'd replace your first indented line with $$w^{*}\equiv argmin\left\{ \frac{1}{2}w'\varSigma w-\lambda\left(w'\mathbf{1}-1\right)\right\}$$ which will give you $$... 1 The derivation is correct and given the formula you should get w^{*'} \vec{1} = 1. My guess is that the inversion of the \Omega matrix is numerically badly conditioned. Instead of implementing the formula as it is, have you tried to calculate \vec{1}^{'} \Omega^{-1} and e^{'} \Omega^{-1} only once and rewrite:$$ w^{*\prime} = \frac{1}{2}\left[ ...

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The paper alternatives between using eigenportfolios and sector/industry ETFs for statistical arbitrage. For instance, sections 2.1-2 vs. 2.3. The trade in Section 4.1 is long some stock and short an appropriate amount of sector/industry ETFs. That being said Sections 5.3 and 5.4 discuss PCA strategies in a backtest, with relatively little additional ...

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