M-Quantile Estimation for GARCH Models

Patrick F. Patrocinio (), Valderio A. Reisen (), Pascal Bondon (), Edson Z. Monte () and Ian M. Danilevicz ()
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Patrick F. Patrocinio: Federal University of Espírito Santo
Valderio A. Reisen: Federal University of Espírito Santo
Pascal Bondon: Université Paris-Saclay, CNRS, CentraleSupélec
Edson Z. Monte: Federal University of Espírito Santo
Ian M. Danilevicz: Federal University of Minas Gerais

Computational Economics, 2024, vol. 63, issue 6, No 3, 2175-2192

Abstract: 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.

Keywords: GARCH; M-estimation; Quantile; Robustness; Outliers; Abrupt observations (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10614-023-10398-z

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