Deep Learning Enhanced Multivariate GARCH

TL;DR

This paper proposes a hybrid deep learning and econometric model, LSTM-BEKK, that improves multivariate volatility forecasting by capturing complex dependence structures in financial data.

Contribution

It introduces a novel LSTM-enhanced BEKK framework that combines neural networks with traditional GARCH models for better volatility modeling.

Findings

Outperforms traditional models in risk forecast accuracy

Effectively captures nonlinear and dynamic dependencies

Maintains interpretability of econometric models

Abstract

This paper introduces a novel multivariate volatility modeling framework, named Long Short-Term Memory enhanced BEKK (LSTM-BEKK), that integrates deep learning into multivariate GARCH processes. By combining the flexibility of recurrent neural networks with the econometric structure of BEKK models, our approach is designed to better capture nonlinear, dynamic, and high-dimensional dependence structures in financial return data. The proposed model addresses key limitations of traditional multivariate GARCH-based methods, particularly in capturing persistent volatility clustering and asymmetric co-movement across assets. Leveraging the data-driven nature of LSTMs, the framework adapts effectively to time-varying market conditions, offering improved robustness and forecasting performance. Empirical results across multiple equity markets confirm that the LSTM-BEKK model achieves superior performance in terms of out-of-sample portfolio risk forecast, while maintaining the interpretability from the BEKK models. These findings highlight the potential of hybrid econometric-deep learning models in advancing financial risk management and multivariate volatility forecasting.