Term structure estimation in the presence of autocorrelation

• The class of affine term structure models (ATSM) is widely studied in finance.
• Estimation of an ATSM requires construction of factors.
• Principal components analysis (PCA) is a popular tool for constructing factors.
• An ATSM which uses PCA produces highly autocorrelated implied errors.
• Parameter estimation is precise in the presence of serially correlated errors.

This paper assesses the effects of autocorrelation on parameter estimates of affine term structure models (ATSM) when principal components analysis is used to extract factors. In contrast to recent studies, we design and run a Monte Carlo experiment that relies on the construction of a simulation design that is consistent with the data, rather than theory or observation, and find that parameter estimation from ATSM is precise in the presence of serial correlation in the measurement error term. Our findings show that parameter estimation of ATSM with principal component based factors is robust to autocorrelation misspecification.