Copula Variational LSTM for High-dimensional Cross-market Multivariate Dependence Modeling
This addresses the challenge of capturing complex dependencies in financial markets for improved portfolio forecasting, representing a novel integration of methods rather than an incremental improvement.
The paper tackles the problem of modeling high-dimensional dependencies across multivariate financial indicators in heterogeneous markets by integrating variational sequential neural learning with copula-based dependence modeling, resulting in a model that outperforms benchmarks like linear models, stochastic volatility models, deep neural networks, and variational recurrent networks in cross-market portfolio forecasting.
We address an important yet challenging problem - modeling high-dimensional dependencies across multivariates such as financial indicators in heterogeneous markets. In reality, a market couples and influences others over time, and the financial variables of a market are also coupled. We make the first attempt to integrate variational sequential neural learning with copula-based dependence modeling to characterize both temporal observable and latent variable-based dependence degrees and structures across non-normal multivariates. Our variational neural network WPVC-VLSTM models variational sequential dependence degrees and structures across multivariate time series by variational long short-term memory networks and regular vine copula. The regular vine copula models nonnormal and long-range distributional couplings across multiple dynamic variables. WPVC-VLSTM is verified in terms of both technical significance and portfolio forecasting performance. It outperforms benchmarks including linear models, stochastic volatility models, deep neural networks, and variational recurrent networks in cross-market portfolio forecasting.