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Forecasting High-Dimensional Portfolios

Raffaele Mattera

Journal of Time Series Econometrics2025https://doi.org/10.1515/jtse-2023-0011article
AJG 2ABDC B
Weight
0.50

What the paper says

Abstract In this paper, we investigate the usefulness of forecasting in a high-dimensional framework where the number of assets is larger than the temporal observations. The benefit of forecasting lies in the concept of timing , which means anticipating future market conditions. We find that when high-dimensional econometric approaches are used, forecasting either the mean or the covariance is better than predicting both and then approaches based on static estimates. Moreover, we find that timing portfolios also perform better than the naive strategy. Considering the portfolio returns over time, we find that a possible explanation for the better performance of volatility-timing portfolios is that they better manage risk during periods of high uncertainty.

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https://doi.org/https://doi.org/10.1515/jtse-2023-0011

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@article{raffaele2025,
  title        = {{Forecasting High-Dimensional Portfolios}},
  author       = {Raffaele Mattera},
  journal      = {Journal of Time Series Econometrics},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1515/jtse-2023-0011},
}

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Forecasting High-Dimensional Portfolios

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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