Prediction and Analysis of Large-Scale Stock Market Risk Correlation Structure

The prediction of the high-dimensional dependence structure among global stock markets is an advanced topic in financial risk management. However, the complex structure characterized by high dimensionality, nonlinearity, and asymmetry, presents significant challenges for conventional financial models (e.g. Autoregressive Moving Average Model) to systematically and efficiently predict and analyze large-scale cross-market correlation structures. The original program for preprocessing the correlation structure decomposition across 36 countries was optimized, enabling it to scale up to hundreds of threads in parallel. It achieved an acceleration of over 25.88 times on 30 threads compared to the original program. The advancement of deep learning technology has provided a solution to this issue. By constructing models such as Recurrent Neural Networks (RNN), Long Short-Term Memory Networks (LSTM), and Gated Recurrent Unit Networks (GRU), along with Average Weighted Ensemble Algorithm(AWEA), Encoder-Decoder Algorithm with Attention(EDAA), and Random Forest Ensemble Algorithm(RFEA), we can achieve financial time series forecasting based on a Patched Bivariate Fréchet Copula model for dependence structures. This provides a rapid decision-making basis for preventing significant financial risks. The experimental results show that the RFEA model outperforms the other two ensemble neural network algorithms in predicting correlation structures. And also the RFEA model showed lower error (e.g., the MSE of global alpha is about 89% of the other two algorithms) and better fit (global alpha and gamma fit to 99.5% and 98.7%, respectively). It demonstrates good predictive performance and high operational efficiency, with certain application value for analyzing the correlation structure of the stock market.