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

Carlos Trucíos (), João H. G. Mazzeu, Marc Hallin, Luiz Hotta, Pedro Valls Pereira and Mauricio Zevallos

No 505, Textos para discussão from FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil)

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.

Date: 2019-06
New Economics Papers: this item is included in nep-rmg
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Citations: View citations in EconPapers (3)

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Related works:
Journal Article: Forecasting Conditional Covariance Matrices in High-Dimensional Time Series: A General Dynamic Factor Approach (2022)
Working Paper: Forecasting Conditional Covariance Matrices in High-Dimensional Time Series: a General Dynamic Factor Approach (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:fgv:eesptd:505

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