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M-Quantile Estimation for GARCH Models

Author

Listed:
  • Patrick F. Patrocinio

    (Federal University of Espírito Santo
    Université Paris-Saclay, CNRS, CentraleSupélec)

  • Valderio A. Reisen

    (Federal University of Espírito Santo
    Federal University of Minas Gerais
    Université Paris-Saclay, CNRS, CentraleSupélec
    Federal University of Bahia)

  • Pascal Bondon

    (Université Paris-Saclay, CNRS, CentraleSupélec)

  • Edson Z. Monte

    (Federal University of Espírito Santo)

  • Ian M. Danilevicz

    (Federal University of Minas Gerais
    Université Paris-Saclay, CNRS, CentraleSupélec)

Abstract

M-regression and quantile methods have been suggested to estimate generalized autoregressive conditionally heteroscedastic (GARCH) models. In this paper, we propose an M-quantile approach, which combines quantile and M-regression to obtain a robust estimator of the conditional volatility when the data have abrupt observations or heavy-tailed distributions. Monte Carlo experiments are conducted to show that the M-quantile approach is more resistant against additive outliers than M-regression and quantile methods. The usefulness of the method is illustrated on two financial datasets.

Suggested Citation

  • Patrick F. Patrocinio & Valderio A. Reisen & Pascal Bondon & Edson Z. Monte & Ian M. Danilevicz, 2024. "M-Quantile Estimation for GARCH Models," Computational Economics, Springer;Society for Computational Economics, vol. 63(6), pages 2175-2192, June.
    • Handle: RePEc:kap:compec:v:63:y:2024:i:6:d:10.1007_s10614-023-10398-z
    DOI: 10.1007/s10614-023-10398-z
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    References listed on IDEAS

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