Best LACSC 2019 Paper award
“Forecasting Conditional Covariance Matrices in High-dimensional Time Series : a General Dynamic Factor Approach“
by Marc Hallin, Luiz K. Hotta, João H. G. Mazzeu, Carlos Trucios, Pedro L. Valls Pereira and Mauricio Zevallos
received the best LACSC 2019 Paper Award at the 4th Latin American Conference for Statistical Computing, held in Guayaquil, Ecuador, May 28-31, 2019.
You can download the paper here.
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.
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