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On the Choice of a Genetic Algorithm for Estimating GARCH Models

Author

Listed:
  • Manuel Rizzo

    (Sapienza University of Rome)

  • Francesco Battaglia

    (Sapienza University of Rome)

Abstract

The GARCH models have been found difficult to build by classical methods, and several other approaches have been proposed in literature, including metaheuristic and evolutionary ones. In the present paper we employ genetic algorithms to estimate the parameters of GARCH(1,1) models, assuming a fixed computational time (measured in number of fitness function evaluations) that is variously allocated in number of generations, number of algorithm restarts and number of chromosomes in the population, in order to gain some indications about the impact of each of these factors on the estimates. Results from this simulation study show that if the main purpose is to reach a high quality solution with no time restrictions the algorithm should not be restarted and an average population size is recommended, while if the interest is focused on driving rapidly to a satisfactory solution then for moderate population sizes it is convenient to restart the algorithm, even if this means to have a small number of generations.

Suggested Citation

  • Manuel Rizzo & Francesco Battaglia, 2016. "On the Choice of a Genetic Algorithm for Estimating GARCH Models," Computational Economics, Springer;Society for Computational Economics, vol. 48(3), pages 473-485, October.
    • Handle: RePEc:kap:compec:v:48:y:2016:i:3:d:10.1007_s10614-015-9522-7
    DOI: 10.1007/s10614-015-9522-7
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    References listed on IDEAS

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    1. Winker, Peter & Gilli, Manfred, 2004. "Applications of optimization heuristics to estimation and modelling problems," Computational Statistics & Data Analysis, Elsevier, vol. 47(2), pages 211-223, September.
    2. Guillermo Santamaría-Bonfil & Juan Frausto-Solís & Ignacio Vázquez-Rodarte, 2015. "Volatility Forecasting Using Support Vector Regression and a Hybrid Genetic Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 45(1), pages 111-133, January.
    3. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    4. Kwami Adanu, 2006. "Optimizing the Garch Model–An Application of Two Global and Two Local Search Methods," Computational Economics, Springer;Society for Computational Economics, vol. 28(3), pages 277-290, October.
    5. Peter Winker & Dietmar Maringer, 2009. "The convergence of estimators based on heuristics: theory and application to a GARCH model," Computational Statistics, Springer, vol. 24(3), pages 533-550, August.
    6. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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