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Genetic algorithm estimation of interest rate term structure

Author

Listed:
  • Ricardo Gimeno

    () (Banco de España)

  • Juan M. Nave

    () (Universidad CEU Cardenal Herrera)

Abstract

The term structure of interest rates is an instrument that gives us the necessary information for valuing deterministic financial cash flows, measuring the economic market expectations and testing the effectiveness of monetary policy decisions. However, it is not directly observable and needs to be measured by smoothing data obtained from asset prices through statistical techniques. Adjusting parsimonious functional forms - as proposed by Nelson and Siegel (1987) and Svensson (1994) - is the most popular technique. This method is based on bond yields to maturity and the high degree of non linearity of the functions to be optimised make it very sensitive to the initial values employed. In this context, this paper proposes the use of genetic algorithms to find these values and reduce the risk of false convergence, showing that stable time series parameters are obtained without the need to impose any kind of restrictions.

Suggested Citation

  • Ricardo Gimeno & Juan M. Nave, 2006. "Genetic algorithm estimation of interest rate term structure," Working Papers 0634, Banco de España;Working Papers Homepage.
  • Handle: RePEc:bde:wpaper:0634
    as

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    File URL: http://www.bde.es/f/webbde/SES/Secciones/Publicaciones/PublicacionesSeriadas/DocumentosTrabajo/06/Fic/dt0634e.pdf
    File Function: First version, December 2006
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Philip Vermeulen & Daniel A. Dias & Maarten Dossche & Erwan Gautier & Ignacio Hernando & Roberto Sabbatini & Harald Stahl, 2012. "Price Setting in the Euro Area: Some Stylized Facts from Individual Producer Price Data," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(8), pages 1631-1650, December.
    2. Kujal Praveen & Ruiz Juan M., 2007. "Cost Effectiveness of R&D and Strategic Trade Policy," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 7(1), pages 1-35, April.
    3. Maravall, A. & del Rio, A., 2007. "Temporal aggregation, systematic sampling, and the Hodrick-Prescott filter," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 975-998, October.
    4. Eder, Armin & Keiler, Sebastian & Pichl, Hannes, 2013. "Interest rate risk and the Swiss solvency test," Discussion Papers 41/2013, Deutsche Bundesbank.
    5. repec:spr:annopr:v:260:y:2018:i:1:d:10.1007_s10479-016-2282-5 is not listed on IDEAS
    6. Paul Beaumont & Yaniv Jerassy-Etzion, 2011. "Computing maximally smooth forward rate curves for coupon bonds: An iterative piecewise quartic polynomial interpolation method," Working Papers wp2011_08_03, Department of Economics, Florida State University.
    7. Julián Andrada-Félix & Adrian Fernandez-Perez & Fernando Fernández-Rodríguez, 2015. "Fixed income strategies based on the prediction of parameters in the NS model for the Spanish public debt market," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 6(2), pages 207-245, June.
    8. Michiel De Pooter, 2007. "Examining the Nelson-Siegel Class of Term Structure Models," Tinbergen Institute Discussion Papers 07-043/4, Tinbergen Institute.
    9. Emma Berenguer-Carceles & Ricardo Gimeno & Juan M. Nave, 2012. "Estimation of the Term Structure of Interest Rates: Methodology and Applications," Working Papers 12.06, Universidad Pablo de Olavide, Department of Financial Economics and Accounting (former Department of Business Administration).

    More about this item

    Keywords

    forward and spot interest rates; Nelson and Siegel model; non-linear optimization; numerical methods; Svensson model; yield curve estimation;

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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