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M-Estimation In Garch Models

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  • Mukherjee, Kanchan

Abstract

This paper derives asymptotic normality of a class of M-estimators in the generalized autoregressive conditional heteroskedastic (GARCH) model. The class of estimators includes least absolute deviation and Huber's estimator in addition to the well-known quasi maximum likelihood estimator. For some estimators, the asymptotic normality results are obtained only under the existence of fractional unconditional moment assumption on the error distribution and some mild smoothness and moment assumptions on the score function.

Suggested Citation

  • Mukherjee, Kanchan, 2008. "M-Estimation In Garch Models," Econometric Theory, Cambridge University Press, vol. 24(6), pages 1530-1553, December.
    • Handle: RePEc:cup:etheor:v:24:y:2008:i:06:p:1530-1553_08
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    Cited by:

    1. Christian Francq & Jean-Michel Zakoïan, 2013. "Optimal predictions of powers of conditionally heteroscedastic processes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(2), pages 345-367, March.
    2. Preminger, Arie & Storti, Giuseppe, 2014. "Least squares estimation for GARCH (1,1) model with heavy tailed errors," MPRA Paper 59082, University Library of Munich, Germany.
    3. Hang Liu & Kanchan Mukherjee, 2022. "R-estimators in GARCH models: asymptotics and applications," The Econometrics Journal, Royal Economic Society, vol. 25(1), pages 98-113.
    4. Farhat Iqbal, 2013. "Robust estimation of the simplified multivariate GARCH model," Empirical Economics, Springer, vol. 44(3), pages 1353-1372, June.

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