Recurrent Neural Network GO-GARCH Model for Portfolio Selection

Area of Science:

• Econometrics
• Computational Finance
• Machine Learning

Background:

• Accurate modeling of multivariate volatility is crucial for financial risk management and portfolio optimization.
• Existing generalized orthogonal GARCH (GO-GARCH) models offer a structured approach but may lack flexibility in capturing complex conditional dynamics.
• Recurrent neural networks (RNNs) excel at modeling sequential data and time-varying patterns, offering potential enhancements to volatility modeling.

Purpose of the Study:

• To develop a novel hybrid model that integrates the strengths of recurrent neural networks (RNNs) with the GO-GARCH framework for multivariate volatility modeling.
• To enhance the flexibility and estimation efficiency of volatility models for a large number of financial assets.
• To evaluate the performance of the proposed hybrid model against established benchmark models in a minimum variance portfolio (MVP) context.

Main Methods:

• A hybrid multivariate volatility model is proposed, combining a GO-GARCH framework with RNNs to model conditional variances of latent orthogonal factors.
• The RNNs are employed to capture the dynamic, time-varying nature of conditional variances, offering greater flexibility than traditional GARCH specifications.
• The model's performance is assessed using a minimum variance portfolio (MVP) scenario, comparing it against relevant benchmark models.

Main Results:

• The hybrid GO-GARCH-RNN model successfully captures the conditional variances of latent orthogonal factors, demonstrating its ability to model complex volatility dynamics.
• The proposed approach balances model flexibility with practical estimation, making it suitable for large-scale financial applications.
• Empirical results show that the hybrid model performs favorably compared to benchmark models in the minimum variance portfolio (MVP) optimization scenario.

Conclusions:

• The developed hybrid model offers a powerful and flexible tool for multivariate volatility forecasting and risk management.
• Integrating RNNs into the GO-GARCH framework provides a significant advancement in modeling conditional covariances for numerous assets.
• The model's superior performance in MVP construction highlights its practical utility in financial decision-making.