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Recurrent Neural Network GO-GARCH Model for Portfolio Selection

Martin Burda & Adrian K. Schroeder

Journal of Time Series Econometrics2024https://doi.org/10.1515/jtse-2023-0012article
AJG 2ABDC B
Weight
0.38

What the paper says

We develop a hybrid model of multivariate volatility that uses recurrent neural networks to capture the conditional variances of latent orthogonal factors in a GO-GARCH framework. Our approach seeks to balance model flexibility with ease of estimation and can be used to model conditional covariances of a large number of assets. The model performs favourably in comparison with relevant benchmark models in a minimum variance portfolio (MVP) scenario.

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https://doi.org/https://doi.org/10.1515/jtse-2023-0012

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@article{martin2024,
  title        = {{Recurrent Neural Network GO-GARCH Model for Portfolio Selection}},
  author       = {Martin Burda & Adrian K. Schroeder},
  journal      = {Journal of Time Series Econometrics},
  year         = {2024},
  doi          = {https://doi.org/https://doi.org/10.1515/jtse-2023-0012},
}

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Recurrent Neural Network GO-GARCH Model for Portfolio Selection

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Evidence weight

0.38

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

F · citation impact0.18 × 0.4 = 0.07
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.