Copula-based high dimensional cross-market dependence modeling

Dependence across multiple financial markets, such as stock and foreign exchange rate markets, is high-dimensional, contains various relationships, and often presents complicated dependence structures and characteristics such as asymmetrical dependence. Modeling such dependence structures is very challenging. Although copula has been demonstrated to be effective in describing dependence between variables in recent studies, building effective dependence structures to address the above complexities significantly challenges existing copula models. In this paper, we propose a new D vine-based model with a bottom-up strategy to construct high-dimensional dependence structures. The new modeling outcomes are applied to trade 15 stock market indices and 10 currency rates over 16 years as a case study. Extensive experimental results show that this model and its intrinsic design significantly outperform typical models and industry baselines, as shown by the log-likelihood and Vuong test, and Value at Risk - a widely used industrial benchmark. Our model provides interpretable knowledge and profound insights into the high-dimensional dependence structures across data sources.