Least squares estimation of ARCH models with missing observations

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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.

Original languageEnglish
Pages (from-to)880-891
Number of pages12
JournalJournal of Time Series Analysis
Volume33
Issue number6
DOIs
StatePublished - 1 Nov 2012

Keywords

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

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