Pair Copula Construction for Dependencies Structure in Cryptocurrencies Trading Index Volumes

The demand for statistically sound decisions and their economic impact on policymaking and trading, involving many variables in trading index volumes, is rapidly increasing. Specifically, the modeling of the dependence structure among a higher number of cryptocurrencies in the crypto market has yet to be thoroughly investigated. Although standard multivariate distributions offer some flexibility properties suitable for modeling large numbers of variables, obtaining reliable features for policymaking in higher dimensional applications and dependence structures becomes challenging. Therefore, constructing pair copulas for higher-dimensional applications and dependence structures offers an alternative solution by using appropriate bivariate copula families in regular vines of a pair copula construction. Thus, we have explored various common vines copula-based models and their structures for selected cryptocurrency trading index volumes. We found the bivariate Gumbel copula model appropriate for modeling the dependence structure between the selected cryptocurrencies' trading index volumes in all cases