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

Based on a General Dynamic Factor Model with infinite-dimensional factor space and MGARCH common shocks, 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, outperforming most alternative methods. The new procedure is used to construct one-step-ahead 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

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Year of publication:	2020
Authors:	Trucíos, Carlos
Other Persons:	Mazzeu, João Henrique Gonçalves (contributor) ; Hallin, Marc (contributor) ; Hotta, Luiz K. (contributor) ; Pereira, Pedro L. Valls (contributor) ; Zevallos, Mauricio (contributor)
Publisher:	[2020]: [S.l.] : SSRN
Subject:	Zeitreihenanalyse \| Time series analysis \| Prognoseverfahren \| Forecasting model \| Korrelation \| Correlation \| Theorie \| Theory

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