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A genetic algorithm estimation of the term structure of interest rates

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  • Gimeno, Ricardo
  • Nave, Juan M.

Abstract

The term structure of interest rates is a key instrument for financial research. It provides relevant information for pricing deterministic financial cash flows, it measures economic market expectations and it is extremely useful when assessing the effectiveness of monetary policy decisions. However, it is not directly observable and needs to be estimated by smoothing asset pricing data through statistical techniques. The most popular techniques adjust parsimonious functional forms based on bond yields to maturity. Unfortunately, these functions, which need to be optimised, are highly non-linear which make them very sensitive to the initial conditions. In this context, this paper proposes the use of genetic algorithms to find the values for the initial conditions and to reduce the risk of false convergence, showing that stable parameters are obtained without imposing arbitrary restrictions.

Suggested Citation

  • Gimeno, Ricardo & Nave, Juan M., 2009. "A genetic algorithm estimation of the term structure of interest rates," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2236-2250, April.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:6:p:2236-2250
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    Cited by:

    1. Vadim Kaushanskiy & Victor Lapshin, 2016. "A nonparametric method for term structure fitting with automatic smoothing," Applied Economics, Taylor & Francis Journals, vol. 48(58), pages 5654-5666, December.
    2. He, Xin-Jiang & Zhu, Song-Ping, 2016. "An analytical approximation formula for European option pricing under a new stochastic volatility model with regime-switching," Journal of Economic Dynamics and Control, Elsevier, vol. 71(C), pages 77-85.
    3. Lapshin, Victor & Sohatskaya, Sofia, 2020. "Choosing the weighting coefficients for estimating the term structure from sovereign bonds," International Review of Economics & Finance, Elsevier, vol. 70(C), pages 635-648.
    4. Marianna Lyra, 2010. "Heuristic Strategies in Finance – An Overview," Working Papers 045, COMISEF.
    5. Bekker, Paul A., 2017. "Interpretable Parsimonious Arbitrage-free Modeling of the Yield Curve," Research Report 17009-EEF, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    6. González-Sánchez, Mariano, 2018. "Causality in the EMU sovereign bond markets," Finance Research Letters, Elsevier, vol. 26(C), pages 281-290.
    7. João Caldeira & Guilherme Moura & André Santos, 2015. "Measuring Risk in Fixed Income Portfolios using Yield Curve Models," Computational Economics, Springer;Society for Computational Economics, vol. 46(1), pages 65-82, June.
    8. Ricardo Gimeno & José Manuel Marqués, 2009. "Extraction of financial market expectations about inflation and interest rates from a liquid market," Working Papers 0906, Banco de España.
    9. Ranik Raaen Wahlstrøm & Florentina Paraschiv & Michael Schürle, 2022. "A Comparative Analysis of Parsimonious Yield Curve Models with Focus on the Nelson-Siegel, Svensson and Bliss Versions," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 967-1004, March.
    10. Giuseppe Arbia & Michele Di Marcantonio, 2015. "Forecasting Interest Rates Using Geostatistical Techniques," Econometrics, MDPI, vol. 3(4), pages 1-28, November.
    11. Maciel, Leandro & Gomide, Fernando & Ballini, Rosangela, 2016. "A differential evolution algorithm for yield curve estimation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 129(C), pages 10-30.
    12. Ricardo Gimeno & Eva Ortega, 2016. "The evolution of inflation expectations in euro area markets," Working Papers 1627, Banco de España.
    13. Juan Ángel García & Ricardo Gimeno, 2014. "Flight-to-liquidity flows in the euro area sovereign debt crisis," Working Papers 1429, Banco de España.

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