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Keywords:

  • Density-based divergence measures;
  • robust estimation of covariance matrix;
  • autocovariance;
  • consistency;
  • asymptotic normality

In this article, we study the robust estimation for the covariance matrix of stationary multi-variate time series. As a robust estimator, we propose to use a minimum density power divergence estimator (MDPDE) proposed by Basu et al. (1998). Particularly, the MDPDE is designed to perform properly when the time series is Gaussian. As a special case, we consider the robust estimator for the autocovariance function of univariate stationary time series. It is shown that the MDPDE is strongly consistent and asymptotically normal under regularity conditions. Simulation results are provided for illustration.