Least squares estimation of ARCH models with missing observations

Pascal Bondon, Natalia Bahamonde

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


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.

Original languageEnglish
Pages (from-to)880-891
Number of pages12
JournalJournal of Time Series Analysis
Issue number6
StatePublished - Nov 2012


  • ARCH models
  • Conditional heteroscedasticity
  • Least squares estimation
  • Martingale central limit theorem
  • Missing observations


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