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Testing For Threshold Nonlinearity in Short-Term Interest Rates

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  • Nikolay Gospodinov

Abstract

This article addresses some empirical problems in the term structure of interest rates using a threshold autoregressive framework with GARCH errors. This framework provides a parsimonious representation of some stylized features of interest rate data and facilitates statistical inference in the presence of high persistence and conditional heteroskedasticity. We propose a bootstrap-based LM test for linearity in the conditional mean and variance functions. The empirical results indicate a presence of threshold nonlinearities in the AR and GARCH representations of the conditional moments of short-term rate. The explicit modeling of these nonlinearities appears to improve the stability properties of the process for spot rate. The article also reports that allowing for threshold nonlinearities in conditional mean and variance leads to significant forecast improvements. The economic significance of these findings is evaluated by the term structure implications of the estimated TAR-GARCH model. Copyright 2005, Oxford University Press.

Suggested Citation

  • Nikolay Gospodinov, 2005. "Testing For Threshold Nonlinearity in Short-Term Interest Rates," Journal of Financial Econometrics, Oxford University Press, vol. 3(3), pages 344-371.
    • Handle: RePEc:oup:jfinec:v:3:y:2005:i:3:p:344-371
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbi016
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    Cited by:

    1. Wolfgang Lemke & Theofanis Archontakis, 2008. "Bond pricing when the short-term interest rate follows a threshold process," Quantitative Finance, Taylor & Francis Journals, vol. 8(8), pages 811-822.
    2. Peter Sephton & Janelle Mann, 2013. "Threshold Cointegration: Model Selection with an Application," Journal of Economics and Econometrics, Economics and Econometrics Society, vol. 56(2), pages 54-77.
    3. Chen, Cathy W.S. & Gerlach, Richard H. & Tai, Amanda P.J., 2008. "Testing for nonlinearity in mean and volatility for heteroskedastic models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(3), pages 489-499.
    4. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    5. Misiorek Adam & Trueck Stefan & Weron Rafal, 2006. "Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(3), pages 1-36, September.
    6. Chew Lian Chua & Sandy Suardi & Sarantis Tsiaplias, 2011. "Predicting Short-Term Interest Rates: Does Bayesian Model Averaging Provide Forecast Improvement?," Melbourne Institute Working Paper Series wp2011n01, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    7. Sephton, Peter & Mann, Janelle, 2018. "Gold and crude oil prices after the great moderation," Energy Economics, Elsevier, vol. 71(C), pages 273-281.
    8. Gospodinov, Nikolay, 2008. "Asymptotic and bootstrap tests for linearity in a TAR-GARCH(1,1) model with a unit root," Journal of Econometrics, Elsevier, vol. 146(1), pages 146-161, September.
    9. Cathy W. S. Chen & Richard H. Gerlach & Ann M. H. Lin, 2010. "Falling and explosive, dormant, and rising markets via multiple‐regime financial time series models," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 26(1), pages 28-49, January.
    10. Jesús Gonzalo & Jean-Yves Pitarakis, 2013. "Estimation and inference in threshold type regime switching models," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 8, pages 189-205, Edward Elgar Publishing.
    11. Leonidov, Andrei & Trainin, Vladimir & Zaitsev, Alexander & Zaitsev, Sergey, 2007. "Market mill dependence pattern in the stock market: Modeling of predictability and asymmetry via multi-component conditional distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 386(1), pages 240-252.
    12. Sephton, Peter S., 2019. "El Niño, La Niña, and a cup of Joe," Energy Economics, Elsevier, vol. 84(C).
    13. Christopoulos, Dimitris & McAdam, Peter & Tzavalis, Elias, 2018. "Dealing with endogeneity in threshold models using copulas: an illustration to the foreign trade multiplier," Working Paper Series 2136, European Central Bank.
    14. Ahmad Yamin & Donayre Luiggi, 2016. "Outliers and persistence in threshold autoregressive processes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(1), pages 37-56, February.

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