Copula Variational LSTM for High-Dimensional Cross-Market Multivariate Dependence Modeling.

Area of Science:

• Quantitative Finance
• Machine Learning
• Statistical Modeling

Background:

• Cross-market modeling (CMM) is crucial for understanding financial couplings and interactions across heterogeneous markets.
• Existing methods struggle with high-dimensional, long-range dependencies and nonnormal multivariate data.

Purpose of the Study:

• To develop a novel approach for modeling complex dependencies in nonnormal multivariate financial data.
• To enhance cross-market modeling (CMM) by integrating deep learning with statistical dependence modeling.

Main Methods:

• We propose the copula variational learning network weighted partial regular vine copula-based variational long short-term memory (WPVC-VLSTM).
• This network combines variational long short-term memory (LSTM) for sequential dependencies and regular vine copula for nonnormal distributional structures.
• WPVC-VLSTM models temporal dependence degrees and structures between hidden variables representing nonnormal multivariates.

Main Results:

• WPVC-VLSTM effectively characterizes both temporal dependence degrees and structures.
• The model captures long-range dependencies in high-dimensional dynamic hidden variables without strong assumptions.
• WPVC-VLSTM significantly outperforms benchmark models in technical significance and portfolio forecasting.

Conclusions:

• WPVC-VLSTM represents a significant advancement in cross-market modeling and deep variational learning.
• The integrated approach offers superior performance for complex financial dependency modeling.
• This method provides a powerful tool for analyzing and forecasting financial markets.