Pair Copula Construction for Dependencies Structure in Cryptocurrencies Trading Volumes

Friday  I. Agu

Authors

Friday I. Agu Department of Sensory Information Systems and Technologies, Institute of Informatics, Slovak Academy of Sciences, Bratislava, Slovakia

Keywords:

Copula model, pair copula construction, D-vine, C-vine, R-vine, dependence measure

Abstract

Understanding how cryptocurrency trading volumes co-move remains challenging, especially in high dimensions where bivariate models fall short. Exploring these dependencies aids risk management, diversification, and policy oversight. This study investigates monthly trading volumes for Binance Coin (BNB), Bitcoin (BTC), TRON (TRX), Ethereum (ETH), and Dogecoin (DOGE) from October 2017 to July 2021. Monthly aggregation dampens microstructure noise and short-lived bursts that distort daily data, while providing far more observations and modeling stability than annual series, which are too coarse for reliable vine estimation. After transforming margins to uniform, we model dependence using pair-copula constructions within D-vine, C-vine, and R-vine frameworks, fitting Student’s t, Clayton, Gumbel, and Frank copulas via sequential estimation. Log-likelihood, BIC evaluates model fit, and dependence is summarized via Kendall’s τ. We also assess trading volume volatility using the mean and standard deviation of monthly changes. Across vine structures, the Gumbel copula consistently provides the best fit, indicating pronounced upper-tail dependence among cryptocurrencies. Conditioning reduces τ at higher vine trees, showing that conditional links capture much of the residual dependence. BTC exhibits the highest volatility risk, followed by DOGE, ETH, TRX, and BNB. These results support stress-testing for simultaneous surges, inform portfolio construction, diversification with lower-dependence pairs, and offer practical guidance for traders and regulators monitoring systemic risk.

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