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Least squares estimation of ARCH models with missing observations

Abstract : A least squares estimator for ARCH models in the presence of missing data is proposed. Strong consistency and asymptotic normality are derived. Monte Carlo simulation results are analysed and an application to real data of a Chilean stock index is reported.
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Contributor : Pascal Bondon <>
Submitted on : Thursday, May 2, 2013 - 11:38:28 AM
Last modification on : Wednesday, September 16, 2020 - 4:44:55 PM

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Pascal Bondon, Natalia Bahamonde. Least squares estimation of ARCH models with missing observations. Journal of Time Series Analysis, Wiley-Blackwell, 2012, 33 (6), pp.880-891. ⟨10.1111/j.1467-9892.2012.00803.x⟩. ⟨hal-00819753⟩

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