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

Journal of Business & Economic Statistics, 2022, vol. 41, issue 1, 40-52

Abstract: 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.

Date: 2022
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Related works:
Working Paper: Forecasting Conditional Covariance Matrices in High-Dimensional Time Series: a General Dynamic Factor Approach (2019)
Working Paper: Forecasting conditional covariance matrices in high-dimensional time series: a general dynamic factor approach (2019)
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DOI: 10.1080/07350015.2021.1996380

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