Term structure estimation in the presence of autocorrelation |
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| Affiliation: | 1. Korea University, Republic of Korea;2. University of Exeter, United Kingdom;1. Department of Mathematics, University of Bayreuth, Germany;2. Department of Economics, University of Bayreuth, Germany;3. Public Choice Research Centre, University of Turku, Finland;1. Assistant Professor Department of Computer Science and Engineering Anna University Regional Office, Madurai, Tamilnadu, India;2. Professor Department of Information Technology K.L.N.College of Engineering, Pottapalayam, Sivaganga, Tamil Nadu, India |
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| Abstract: | 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. |
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| Keywords: | Affine term structure model Principal components analysis Autocorrelation misspecification Monte Carlo simulation Maximum likelihood estimation |
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