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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.

New Economics Papers: this item is included in nep-rmg
Date: 2019-06
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Working Paper: Forecasting Conditional Covariance Matrices in High-Dimensional Time Series: a General Dynamic Factor Approach (2019) Downloads
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