Evolutionary Intelligence

, Volume 9, Issue 4, pp 137–151 | Cite as

Selecting and estimating interest rate models with evolutionary methods

  • Dietmar MaringerEmail author
  • Sebastian H. M. Deininger
Special Issue


Selecting and estimating parsimonious models is often desired, but hard to achieve. This is particularly true when models can potentially contain a very large number of parameters but data are scarce—as is the case for many macro-economic models in general and interest-rate models in particular. These models need to cater for a large number of potential relationships and dependencies, but are fitted on low-frequency data to focus on the bigger picture and long-term effects. To identify the ideal model and estimating it is then particularly demanding from an optimization perspective. In this paper, we suggest an evolutionary approach that considers model selection and estimation simultaneously. Numerical experiments with artificial data suggest that the approach is well suited for this type of problem. In an empirical application for short-term and long-term interest rates denominated in US dollar, euro and the Japanese yen, respectively, parsimonious model structures are identified that highlight the dependencies as well as spill-overs across maturities and currencies.


Differential evolution Interest rate models Econometrics Vector error correction models 



We are grateful to the anonymous referees for their valuable comments and suggestions; to seminar and conference audiences in London, Klagenfurt, Geneva, and Lisbon for their feedback; to Peter Winker and Sandra Paterlini for discussions and inputs; and to the editors of this special issue for their encouragement and support.


  1. 1.
    Brabazon A, O’Neill M, McGarraghy S (2015) Natural computing algorithms. Springer, BerlinCrossRefzbMATHGoogle Scholar
  2. 2.
    Brooks C, Burke SP, Persand G (2001) Benchmark and the accuracy of GARCH model estimation. Int J Forecast 17:45–56CrossRefGoogle Scholar
  3. 3.
    Cecchetti SG, Schoenholtz KL (2011) Money, banking, and financial markets, global edition, 3rd edn. McGraw Hill, New YorkGoogle Scholar
  4. 4.
    Dornbusch R (1990) Real exchange rates and macroeconomics: a selective survey. Working paper 2775, National Bureau of Economic Research.
  5. 5.
    Engle RF, Granger CWJ (1987) Co-integration and error correction: representation, estimation, and testing. Econometrica 55(2):251–76.
  6. 6.
    Hastie T, Tibshirani R, Friedman J (2013) The elements of statistical learning: data mining, inference, and prediction, 2nd edn. Springer, Berlin.,
  7. 7.
    Juselius K (1993) Do purchasing power parity and uncovered interest rate parity hold in the long run?—an example of likelihood in a multivariate time-series model. Discussion papers 93–14, University of Copenhagen. Department of Economics
  8. 8.
    Lütkepohl H (2007) New introduction to multiple times series analysis, 2nd edn. Springer, BerlinGoogle Scholar
  9. 9.
    MacDonald R, Marsh I (1999) Exchange rate modelling. Advanced studies in theoretical and applied econometrics. Kluwer Academic Publishers, Dordrecht.
  10. 10.
    McCullough B, Renfro C (1999) Benchmarks and software standards: a case study for GARCH procedures. Comput Econ Soc Meas 25:59–71Google Scholar
  11. 11.
    McCullough B, Wilson B (2005) On the accuracy of statistical procedures in Microsoft Excel 2003. Comput Stat Data Anal 49(4):1244–1252MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Price KV, Storn RM, Lampinen JA (2005) Differential evolution. A practical approach to global optimization. Springer, BerlinzbMATHGoogle Scholar
  13. 13.
    Storn R, Price K (1997) Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359. doi: 10.1023/A:1008202821328 MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Weber E (2006) British interest rate convergence between the US and europe: a recursive cointegration analysis. IUP J Monet Econ IV(4):29–47.
  15. 15.
    Winker P (2001) Optimization heuristics in econometrics: applications of threshold accepting. Wiley, ChichesterzbMATHGoogle Scholar
  16. 16.
    Winker P, Maringer D (2004) New directions in macromodelling. In: Welfe A (ed) New directions in macromodeling. Chap optimal lag structure selection in VAR and VEC models, Elsevier, Amsterdam, pp 213–234CrossRefGoogle Scholar
  17. 17.
    Winker P, Maringer D (2007) The threshold accepting optimisation algorithm in economics and statistics. In: Kontoghiorghes EJ, Gatu C (eds) Optimisation, econometric and financial analysis. Springer, Berlin, pp 107–125. doi: 10.1007/3-540-36626-1-6
  18. 18.
    Winker P, Maringer D (2009) The convergence of estimators based on heuristics: theory and application to a GARCH model. Comput Stat 24(3):533–550. doi: 10.1007/s00180-008-0145-5 MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Faculty of Business and EconomicsBaselSwitzerland

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