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Forecasting conditional covariance matrices in high-dimensional time series: a general dynamic factor approach

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Listed:
  • Trucíos, Carlos
  • Mazzeu, João H. G.
  • Hallin, Marc
  • Hotta, Luiz K.
  • Pereira, Pedro L. Valls
  • Zevallos, Mauricio

Abstract

Based on a General Dynamic Factor Model with infinite-dimensional factor space, we develop a new estimation and forecasting procedures for conditional covariance matrices in high-dimensional time series. The performance of our approach is evaluated via Monte Carlo experiments, outperforming many alternative methods. The new procedure is used to construct minimum variance portfolios for a high-dimensional panel of assets. The results are shown to achieve better out-of-sample portfolio performance than alternative existing procedures.

Suggested Citation

  • Trucíos, Carlos & Mazzeu, João H. G. & Hallin, Marc & Hotta, Luiz K. & Pereira, Pedro L. Valls & Zevallos, Mauricio, 2019. "Forecasting conditional covariance matrices in high-dimensional time series: a general dynamic factor approach," Textos para discussão 505, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
  • Handle: RePEc:fgv:eesptd:505
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