Recurrent Neural Network GO-GARCH Model for Portfolio Selection

被引：0

作者：

Burda, Martin ^{[1
]}

Schroeder, Adrian K. ^{[1
]}

机构：

[1] Univ Toronto, Dept Econ, 150 St George St, Toronto, ON M5S 3G7, Canada

来源：

JOURNAL OF TIME SERIES ECONOMETRICS | 2024年 / 16卷 / 02期

关键词：

LSTM; machine learning; nonlinear time series; multivariate volatility forecasting; C32; C45; G11; G12; MULTIVARIATE; MOMENTS; LSTM; VOLATILITY;

D O I：

10.1515/jtse-2023-0012

中图分类号：

O1 [数学]; C [社会科学总论];

学科分类号：

03 ; 0303 ; 0701 ; 070101 ;

摘要：

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.

引用

页码：67 / 81

页数：15

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