Article

Bayesian Hierarchical Modelling of Cryptocurrency Volatility and Its Spillover to the Digital Financial Economy

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Abstract

The increasing volatility of cryptocurrency markets and their integration into the broader digital financial ecosystem have raised significant concerns regarding systemic risk and financial stability. Despite extensive research on volatility dynamics, existing models often treat digital assets as independent entities, overlooking their hierarchical relationships and cross-market dependencies. This study develops a Bayesian Hierarchical Stochastic Volatility (SV) model combined with a Time-Varying Parameter Vector Autoregression (TVP-VAR) framework to examine how cryptocurrency volatility propagates into the digital financial economy. The hierarchical Bayesian structure enables partial pooling across asset categories such as Layer-1 tokens, DeFi protocols, and payment-oriented coins, while the TVP-VAR component quantifies dynamic spillovers through Generalized Forecast Error Variance Decomposition (GFEVD). Using daily data from 2019–2025 encompassing cryptocurrencies, fintech indices, digital payment volumes, and DeFi metrics, the model achieves strong posterior convergence R ̂<1.01 and demonstrates that major cryptocurrencies, particularly Bitcoin and Ethereum, act as dominant volatility transmitters. The Total Connectedness Index (TCI) averaged 72%, revealing substantial and persistent integration between crypto and fintech sectors. Robustness checks with alternative priors and volatility proxies confirm the model’s stability. These findings highlight that volatility shocks within the crypto ecosystem increasingly influence broader digital financial infrastructures, underscoring the need for integrated monitoring of risk transmission across decentralized and traditional digital markets.

Keywords: Bayesian Hierarchical Modelling, Stochastic Volatility, Cryptocurrency Spillover, Digital Financial Economy, Time-Varying Connectedness

How to Cite:

Sadat, A. M., Saleh, H. N., Maupa, H. & Cokki, , (2025) “Bayesian Hierarchical Modelling of Cryptocurrency Volatility and Its Spillover to the Digital Financial Economy”, FinTech Innovation Journal 1(4), 345-360. doi: https://doi.org/10.63913/ftij.v1i4.86