Forecasting Conditional Covariance Matrices in High-Dimensional Time Series: A General Dynamic Factor Approach. Issue 1 (13th December 2022)

• Record Type: Journal Article
• Authors: Trucíos, Carlos; Mazzeu, João H. G.; Hallin, Marc; Hotta, Luiz K.; Valls Pereira, Pedro L.; Zevallos, Mauricio
• Is Part Of: Journal of business & economic statistics. Volume 41:Issue 1(2023)
• Page Start: 40
• Page End: 52
• Publication Date: 2022-12-13
• Subjects: Dimension reduction -- High-dimensional time series -- Large covariance matrix -- Large panels -- Minimum variance portfolio -- Multivariate GARCH -- Volatility; Economics -- Statistical methods -- Periodicals; Commercial statistics -- Periodicals; Économie politique -- Méthodes statistiques -- Périodiques; Statistique commerciale -- Périodiques; 330.015195
• DOI: 10.1080/07350015.2021.1996380
• Languages: English
• ISSNs: 0735-0015

Abstract

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.