  Principal Component Analysis (PCA) is the most common nonparametric method
for estimating the volatility structure of Gaussian interest rate models. One
major difficulty in the estimation of these models is the fact that forward
rate curves are not directly observable from the market so that non-trivial
observational errors arise in any statistical analysis. In this work, we point
out that the classical PCA analysis is not suitable for estimating factors of
forward rate curves due to the presence of measurement errors induced by market
microstructure effects and numerical interpolation. Our analysis indicates that
the PCA based on the long-run covariance matrix is capable to extract the true
covariance structure of the forward rate curves in the presence of
observational errors. Moreover, it provides a significant reduction in the
pricing errors due to noisy data typically founded in forward rate curves.
