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Least squares estimation of ARCH models with missing observations


Pascal Bondon, CNRS UMR 8506, Université Paris XI, France.


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.