用于投资组合选择的循环神经网络 GO-GARCH 模型

Pub Date : 2024-07-16 DOI:10.1515/jtse-2023-0012

Martin Burda, Adrian K. Schroeder

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引用次数: 0

摘要

我们建立了一个多变量波动率混合模型，该模型使用递归神经网络在 GO-GARCH 框架下捕捉潜在正交因子的条件方差。我们的方法力求在模型的灵活性和估算的简便性之间取得平衡，可用于对大量资产的条件协方差进行建模。在最小方差组合（MVP）情况下，与相关基准模型相比，该模型表现良好。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Recurrent Neural Network GO-GARCH Model for Portfolio Selection

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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