Recurrent Neural Network GO-GARCH Model for Portfolio Selection

Burda Martin () and Schroeder Adrian K. ()
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Burda Martin: Department of Economics, University of Toronto, 150 St. George St., Toronto, ON, M5S 3G7, Canada
Schroeder Adrian K.: Department of Economics, University of Toronto, 150 St. George St., Toronto, ON, M5S 3G7, Canada

Journal of Time Series Econometrics, 2024, vol. 16, issue 2, 67-81

Abstract: 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.

Keywords: LSTM; machine learning; nonlinear time series; multivariate volatility forecasting (search for similar items in EconPapers)
JEL-codes: C32 C45 G11 G12 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1515/jtse-2023-0012

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