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Evolving fuzzy modelling for yield curve forecasting

Author

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  • Leandro Maciel
  • Rosangela Ballini
  • Fernando Gomide

Abstract

Forecasting the term structure of interest rates plays a crucial role in portfolio management, household finance decisions, business investment planning, and policy formulation. This paper aims to address yield curve forecasting and evolving fuzzy systems modelling using data from US and Brazilian fixed income markets. Evolving fuzzy models provide a high level of system adaptation and learn the system dynamic continuously, which is essential for uncertain environments as interest rate markets. Computational experiments show that the evolving fuzzy modelling approaches describe the interest rate behaviour accurately, outperforming traditional econometric techniques in terms of error measures and statistical tests. Moreover, evolving models provide promising results for short and long-term maturities and for both fixed income markets evaluated, highlighting its potential to forecast complex nonlinear dynamics in uncertain environments.

Suggested Citation

  • Leandro Maciel & Rosangela Ballini & Fernando Gomide, 2018. "Evolving fuzzy modelling for yield curve forecasting," International Journal of Economics and Business Research, Inderscience Enterprises Ltd, vol. 15(3), pages 290-311.
    • Handle: RePEc:ids:ijecbr:v:15:y:2018:i:3:p:290-311
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    Cited by:

    1. Helena Gaspars-Wieloch, 2020. "A New Application for the Goal Programming—The Target Decision Rule for Uncertain Problems," JRFM, MDPI, vol. 13(11), pages 1-14, November.
    2. Gaspars-Wieloch Helena, 2021. "On some analogies between one-criterion decision making under uncertainty and multi-criteria decision making under certainty," Economics and Business Review, Sciendo, vol. 7(2), pages 17-36, June.

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