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Forecasting interest rate swap spreads using domestic and international risk factors: Evidence from linear and non-linear models

Author

Listed:
  • Costas Milas

    (Keele University, Centre for Economic Research and School of Economic and Management Studies)

  • Ilias Lekkos

    (Research Department, Eurobank Ergasias, Greece)

  • Theodore Panagiotidis

    (Department of Economics, Loughborough University, UK)

Abstract

This paper explores the ability of factor models to predict the dynamics of US and UK interest rate swap spreads within a linear and a non-linear framework. We reject linearity for the US and UK swap spreads in favour of a regime-switching smooth transition vector autoregressive (STVAR) model, where the switching between regimes is controlled by the slope of the US term structure of interest rates. We compare the ability of the STVAR model to predict swap spreads with that of a non-linear nearest-neighbours model as well as that of linear AR and VAR models.We find some evidence that the non-linear models predict better than the linear ones. At short horizons, the nearest-neighbours (NN) model predicts better than the STVAR model US swap spreads in periods of increasing risk conditions and UK swap spreads in periods of decreasing risk conditions. At long horizons, the STVAR model increases its forecasting ability over the linear models, whereas the NN model does not outperform the rest of the models.

Suggested Citation

  • Costas Milas & Ilias Lekkos & Theodore Panagiotidis, 2006. "Forecasting interest rate swap spreads using domestic and international risk factors: Evidence from linear and non-linear models," Keele Economics Research Papers KERP 2006/05, Centre for Economic Research, Keele University.
    • Handle: RePEc:kee:kerpuk:2006/05
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    2. Chung, Hon-Lun & Chan, Wai-Sum, 2010. "Impact of credit spreads, monetary policy and convergence trading on swap spreads," International Review of Financial Analysis, Elsevier, vol. 19(2), pages 118-126, March.
    3. Li, Shaoyu & Zhu, Chunhui & Shang, Yuhuang, 2023. "Hedging demand and near-zero swap spreads: Evidence from the Chinese interest rate swap market," The Quarterly Review of Economics and Finance, Elsevier, vol. 91(C), pages 170-185.
    4. Kim, Jong-Min & Kim, Dong H. & Jung, Hojin, 2021. "Applications of machine learning for corporate bond yield spread forecasting," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    5. Man Wang & Kun Chen & Qin Luo & Chao Cheng, 2018. "Multi-Step Inflation Prediction with Functional Coefficient Autoregressive Model," Sustainability, MDPI, vol. 10(6), pages 1-16, May.
    6. Sohel Azad, A.S.M. & Batten, Jonathan A. & Fang, Victor & Wickramanayake, Jayasinghe, 2015. "International swap market contagion and volatility," Economic Modelling, Elsevier, vol. 47(C), pages 355-371.

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    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects

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