An evaluation of alternative methods used in the estimation of Gaussian term structure models

An evaluation of alternative methods used in the estimation of Gaussian term structure models This paper provides an evaluation of five methods, proposed in the literature, for extracting factors used in the estimation of Gaussian affine term structure models. We assert that irrespective of the method used for extracting state variables, cross-sectional and serial correlations exist in measurement errors. However, using a simulation design which is consistent with the data, we show that parameter estimation using the Kalman filter and the model-free method are quite precise in the presence of serial and cross-sectional correlations in the error term, and, in the presence of different measurement errors, the Kalman filter demonstrates strong empirical tractability. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Review of Quantitative Finance and Accounting Springer Journals

An evaluation of alternative methods used in the estimation of Gaussian term structure models

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Publisher
Springer US
Copyright
Copyright © 2013 by Springer Science+Business Media New York
Subject
Economics / Management Science; Finance/Investment/Banking; Accounting/Auditing; Econometrics; Operations Research/Decision Theory
ISSN
0924-865X
eISSN
1573-7179
D.O.I.
10.1007/s11156-013-0396-2
Publisher site
See Article on Publisher Site

Abstract

This paper provides an evaluation of five methods, proposed in the literature, for extracting factors used in the estimation of Gaussian affine term structure models. We assert that irrespective of the method used for extracting state variables, cross-sectional and serial correlations exist in measurement errors. However, using a simulation design which is consistent with the data, we show that parameter estimation using the Kalman filter and the model-free method are quite precise in the presence of serial and cross-sectional correlations in the error term, and, in the presence of different measurement errors, the Kalman filter demonstrates strong empirical tractability.

Journal

Review of Quantitative Finance and AccountingSpringer Journals

Published: Aug 18, 2013

References

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