IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v53y2009i6p2236-2250.html
   My bibliography  Save this article

A genetic algorithm estimation of the term structure of interest rates

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(08)00499-4
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Francis X. Diebold & Monika Piazzesi & Glenn D. Rudebusch, 2005. "Modeling Bond Yields in Finance and Macroeconomics," American Economic Review, American Economic Association, vol. 95(2), pages 415-420, May.
    2. Yang, Zheng & Tian, Zheng & Yuan, Zixia, 2007. "GSA-based maximum likelihood estimation for threshold vector error correction model," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 109-120, September.
    3. Baragona, R. & Battaglia, F. & Cucina, D., 2004. "Fitting piecewise linear threshold autoregressive models by means of genetic algorithms," Computational Statistics & Data Analysis, Elsevier, vol. 47(2), pages 277-295, September.
    4. Francis X. Diebold & Lei Ji & Canlin Li, 2006. "A Three-Factor Yield Curve Model: Non-Affine Structure, Systematic Risk Sources and Generalized Duration," Chapters, in: Lawrence R. Klein (ed.), Long-run Growth and Short-run Stabilization, chapter 9, Edward Elgar Publishing.
    5. Nicola Anderson & John Sleath, 2001. "New estimates of the UK real and nominal yield curves," Bank of England working papers 126, Bank of England.
    6. Chambers, Donald R. & Carleton, Willard T. & Waldman, Donald W., 1984. "A New Approach to Estimation of the Term Structure of Interest Rates," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 19(3), pages 233-252, September.
    7. Unknown, 2005. "Forward," 2005 Conference: Slovenia in the EU - Challenges for Agriculture, Food Science and Rural Affairs, November 10-11, 2005, Moravske Toplice, Slovenia 183804, Slovenian Association of Agricultural Economists (DAES).
    8. Diebold, Francis X. & Li, Canlin, 2006. "Forecasting the term structure of government bond yields," Journal of Econometrics, Elsevier, vol. 130(2), pages 337-364, February.
    9. McCulloch, J Huston, 1971. "Measuring the Term Structure of Interest Rates," The Journal of Business, University of Chicago Press, vol. 44(1), pages 19-31, January.
    10. Mansi, Sattar A & Phillips, Jeffrey H, 2001. "Modeling the Term Structure from the On-the-Run Treasury Yield Curve," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 24(4), pages 545-564, Winter.
    11. Diebold, Francis X. & Rudebusch, Glenn D. & Borag[caron]an Aruoba, S., 2006. "The macroeconomy and the yield curve: a dynamic latent factor approach," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 309-338.
    12. Alcock, Jamie & Burrage, Kevin, 2004. "A genetic estimation algorithm for parameters of stochastic ordinary differential equations," Computational Statistics & Data Analysis, Elsevier, vol. 47(2), pages 255-275, September.
    13. Gilli, Manfred & Winker, Peter, 2007. "2nd Special Issue on Applications of Optimization Heuristics to Estimation and Modelling Problems," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 2-3, September.
    14. Sattar A. Mansi & Jeffery H. Phillips, 2001. "Modeling The Term Structure From The On-The-Run Treasury Yield Curve," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 24(4), pages 545-564, December.
    15. David Bolder & David Stréliski, 1999. "Yield Curve Modelling at the Bank of Canada," Technical Reports 84, Bank of Canada.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. González-Sánchez, Mariano, 2018. "Causality in the EMU sovereign bond markets," Finance Research Letters, Elsevier, vol. 26(C), pages 281-290.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Giuseppe Arbia & Michele Di Marcantonio, 2015. "Forecasting Interest Rates Using Geostatistical Techniques," Econometrics, MDPI, vol. 3(4), pages 1-28, November.
    10. 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.
    11. Marianna Lyra, 2010. "Heuristic Strategies in Finance – An Overview," Working Papers 045, COMISEF.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Leo Krippner, 2008. "A Macroeconomic Foundation for the Nelson and Siegel Class of Yield Curve Models," Research Paper Series 226, Quantitative Finance Research Centre, University of Technology, Sydney.
    2. 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).
    3. Koo, B. & La Vecchia, D. & Linton, O., 2019. "Nonparametric Recovery of the Yield Curve Evolution from Cross-Section and Time Series Information," Cambridge Working Papers in Economics 1916, Faculty of Economics, University of Cambridge.
    4. Koo, Bonsoo & La Vecchia, Davide & Linton, Oliver, 2021. "Estimation of a nonparametric model for bond prices from cross-section and time series information," Journal of Econometrics, Elsevier, vol. 220(2), pages 562-588.
    5. Lauren Stagnol, 2017. "Introducing global term structure in a risk parity framework," Working Papers hal-04141648, HAL.
    6. Luis Ceballos & Alberto Naudon & Damián Romero, 2016. "Nominal term structure and term premia: evidence from Chile," Applied Economics, Taylor & Francis Journals, vol. 48(29), pages 2721-2735, June.
    7. Emrah Ahi & Vedat Akgiray & Emrah Sener, 2018. "Robust term structure estimation in developed and emerging markets," Annals of Operations Research, Springer, vol. 260(1), pages 23-49, January.
    8. Coroneo, Laura & Nyholm, Ken & Vidova-Koleva, Rositsa, 2011. "How arbitrage-free is the Nelson-Siegel model?," Journal of Empirical Finance, Elsevier, vol. 18(3), pages 393-407, June.
    9. Varga, Gyorgy, 2009. "Teste de Modelos Estatísticos para a Estrutura a Termo no Brasil," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 63(4), December.
    10. Varga, Gyorgy, 2009. "Teste de Modelos Estatísticos para a Estrutura a Termo no Brasil [Test of Term Structure Models for Brazil]," MPRA Paper 20832, University Library of Munich, Germany.
    11. Chadha, Jagjit S. & Waters, Alex, 2014. "Applying a macro-finance yield curve to UK quantitative Easing," Journal of Banking & Finance, Elsevier, vol. 39(C), pages 68-86.
    12. 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.
    13. Lauren Stagnol, 2019. "Extracting global factors from local yield curves," Journal of Asset Management, Palgrave Macmillan, vol. 20(5), pages 341-350, September.
    14. Faria, Adriano & Almeida, Caio, 2018. "A hybrid spline-based parametric model for the yield curve," Journal of Economic Dynamics and Control, Elsevier, vol. 86(C), pages 72-94.
    15. Michiel De Pooter, 2007. "Examining the Nelson-Siegel Class of Term Structure Models," Tinbergen Institute Discussion Papers 07-043/4, Tinbergen Institute.
    16. Nagano, Teppei & Baba, Naohiko, 2008. "Extracting market expectations from yield curves augmented by money market interest rates: the case of Japan," Working Paper Series 980, European Central Bank.
    17. Eric Hillebrand & Huiyu Huang & Tae-Hwy Lee & Canlin Li, 2018. "Using the Entire Yield Curve in Forecasting Output and Inflation," Econometrics, MDPI, vol. 6(3), pages 1-27, August.
    18. Matsumura, Marco & Moreira, Ajax & Vicente, José, 2011. "Forecasting the yield curve with linear factor models," International Review of Financial Analysis, Elsevier, vol. 20(5), pages 237-243.
    19. Huse, Cristian, 2011. "Term structure modelling with observable state variables," Journal of Banking & Finance, Elsevier, vol. 35(12), pages 3240-3252.
    20. Hautsch, Nikolaus & Yang, Fuyu, 2012. "Bayesian inference in a Stochastic Volatility Nelson–Siegel model," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3774-3792.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:53:y:2009:i:6:p:2236-2250. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.