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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.

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Bibliographic Info

Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

Volume (Year): 53 (2009)
Issue (Month): 6 (April)
Pages: 2236-2250

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Handle: RePEc:eee:csdana:v:53:y:2009:i:6:p:2236-2250

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  1. Francis X. Diebold & Monika Piazzesi & Glenn Rudebusch, 2005. "Modeling Bond Yields in Finance and Macroeconomics," NBER Working Papers 11089, National Bureau of Economic Research, Inc.
  2. Nicola Anderson & John Sleath, 2001. "New estimates of the UK real and nominal yield curves," Bank of England working papers 126, Bank of England.
  3. 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.
  4. Francis X. Diebold & Canlin Li, 2003. "Forecasting the Term Structure of Government Bond Yields," NBER Working Papers 10048, National Bureau of Economic Research, Inc.
  5. 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(03), pages 233-252, September.
  6. 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.
  7. 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.
  8. 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.
  9. Bolder, David & Streliski, David, 1999. "Yield Curve Modelling at the Bank of Canada," Technical Reports 84, Bank of Canada.
  10. 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.
  11. Francis X. Diebold & Lei Ji & Canlin Li, 2006. "A Three-Factor Yield Curve Model: Non-Affine Structure, Systematic Risk Sources, and Generalized Duration," PIER Working Paper Archive 06-017, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  12. 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.
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Cited by:
  1. Ricardo Gimeno & José Manuel Marqués, 2009. "Extraction of financial market expectations about inflation and interest rates from a liquid market," Banco de Espa�a Working Papers 0906, Banco de Espa�a.
  2. Marianna Lyra, 2010. "Heuristic Strategies in Finance – An Overview," Working Papers 045, COMISEF.

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