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Keywords:

  • ARCH models;
  • missing observations;
  • conditional heteroscedasticity;
  • least squares estimation;
  • martingale central limit theorem
  • Primary 62M10;
  • secondary 62F12

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.