Interest rate time series seems to be non-stationary whenever test is performed

But covariance or correlation matrix is derived from term structure time series which are non stationary and later PCA is performed on that covariance or correlation matrix.

Is it appropriate to derive Covariance or correlation matrix from non stationary series and use it for PCA?

Applying Kalman filter on term structure of interest rates is any better than PCA?


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 covariance matrices mean?

  • $\begingroup$ So even individual time series are non stationary but their covariance (spread) is stationary then PCA makes sense?! Thanks $\endgroup$ Nov 20 '14 at 10:25
  • $\begingroup$ Are the increments of your time series non-stationary? It is similar to a random walk. If you have white noise $X_i$ then $S_n = \sum_{i=1}^n X_i$ is non-stationary but $X_i$ is. $\endgroup$
    – Ric
    Nov 20 '14 at 11:29

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