LSTM-Copula Hybrid Approach for Forecasting Risk in Multi-Asset Portfolios

This study proposes a multi-asset portfolio risk prediction model that integrates Long Short-Term Memory (LSTM) networks with Copula functions. The model is designed to capture both the temporal dynamics of financial asset returns and the nonlinear dependencies among assets. LSTM is used to model the marginal distributions of individual asset return series. Copula functions are employed to describe the joint distribution structure across multiple assets. This allows for accurate prediction of key portfolio risk metrics such as Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR). In the experimental design, several baseline models are constructed for performance comparison. Further analyses are conducted to assess the model's risk prediction ability under varying numbers of assets and to evaluate risk coverage across different confidence intervals. The experimental results show that the proposed LSTM-Copula model outperforms mainstream methods across multiple evaluation metrics. It demonstrates higher robustness and predictive accuracy, particularly in high-dimensional and sparse data settings. This approach offers a novel path for financial risk management by combining statistical modeling with deep learning. It provides strong empirical results and holds substantial practical value for applications in complex financial environments.