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Forecasting Conditional Covariance Matrices in High-Dimensional Time Series: A General Dynamic Factor Approach

par Trucíos, Carlos;Mazzeu, João Henrique Gonçalves;Hallin, Marc ;Hotta, Luiz Koodi;Valls Pereira, Pedro L.;Zevallos, Mauricio
Référence Journal of business & economic statistics, 41, page (40--52)
Publication Publié, 2021-04-01

Article révisé par les pairs

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Résumé :

Based on a General Dynamic Factor Model with infinite-dimensional factor space and MGARCH volatility models, we develop new estimation and forecasting procedures for conditional covariance matrices in high-dimensional time series. The finite-sample performance of our approach is evaluated via Monte Carlo experiments and outperforms the most alternative methods. This new approach is also used to construct minimum one-step-ahead variance portfolios for a high-dimensional panel of assets. The results are shown to match the results of recent proposals by Engle, Ledoit, and Wolf and achieve better out-of-sample portfolio performance than alternative procedures proposed in the literature.