Recurrent Neural Network GO-GARCH Model for Portfolio Selection-Reference-Cited by-同舟云学术

Recurrent Neural Network GO-GARCH Model for Portfolio Selection

Published:2024-07-01 Issue:2 Volume:16 Page:67-81
ISSN:2194-6507
Container-title:Journal of Time Series Econometrics
language:en
Short-container-title:

Author:

Burda Martin¹,Schroeder Adrian K.¹

Affiliation:

1. Department of Economics , University of Toronto , 150 St. George St. , Toronto , ON , M5S 3G7 , Canada

Abstract

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.

Funder

Social Sciences and Humanities Research Council of Canada

Publisher

Walter de Gruyter GmbH

Link

https://www.degruyter.com/document/doi/10.1515/jtse-2023-0012/pdf

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