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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.