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In this paper, we analyse the recently procedure of Hu and Tsay (2014) (Principal
volatility component analysis. JBES, v32.2) and Li et al. (2016) (Modeling multivariate
volatilities via latent common factors. JBES, v34.4) called principal volatility
components. This procedure overcomes several diculties in modelling and forecasting
the conditional covariance matrix in large dimensions arising from the curse of
dimensionality. We show that outliers have a devastating eect on the construction
of the principal volatility components and on the forecast of the conditional covariance
matrix and consequently in economic and nancial applications based on this
forecast. We propose a robust procedure and analyse its nite sample properties by
means of Monte Carlo experiments and also illustrate it using empirical data. The
robust procedure outperforms the classical method in simulated and empirical data.
