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Review of Quantitative Finance and Accounting

, Volume 50, Issue 3, pp 695–715 | Cite as

Empirical performance of Gaussian affine dynamic term structure models in the presence of autocorrelation misspecification bias

  • Januj Juneja
Original Research

Abstract

Recently, several authors have documented the presence of estimation bias in Gaussian affine dynamic term structure models (GADTSM). However, only a few applications involving its impact on the empirical performance of GADTSM exist in the extant literature, and these studies focus solely on discrete-time vector autoregressive (VAR) based GADTSM and concentrate on issues of small-sample bias and persistence. In this paper, we provide a comprehensive investigation of this issue that includes the estimation of both discrete-time VAR based GADTSM and continuous-time GADTSM at multiple data frequencies through a unique empirical design and two Monte Carlo simulation experiments, within which we construct estimation bias from the serial correlation in yield pricing errors. Our findings show that, although, empirical performance of all studied GADTSM are severely impacted by estimation bias, discrete-time GADTSM are more severely impacted by estimation bias than continuous-time GADTSM. Building on theoretical arguments developed in previous works, we attribute this finding to the strong dependence of discrete-time VAR based GADTSM on the ordinary least squares econometric technique relative to the continuous-time GADTSM for which general maximum likelihood estimation is more suitable.

Keywords

Dynamic term structure model Autocorrelation misspecification Estimation bias Model performance 

JEL Classification

E43 C52 C58 E47 

Notes

Acknowledgements

I would like to thank Chris Lamoureux, A.D. Amar and seminar participants at the 2015 Financial Management Association Annual meetings held in Orlando, Florida. I would also like to especially thank the editor, Cheng Few Lee, the associate editors, and anonymous referees who are all associated with the Review of Quantitative Finance and Accounting for helpful and insightful comments that greatly improved the paper.

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of Finance, College of Business AdministrationSan Diego State UniversitySan DiegoUSA

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