Cryptocurrency volatility spillovers in emerging markets: a dynamic connectedness analysis

This study investigates the dynamic and asymmetric spillover effects of cryptocurrency volatility on traditional financial markets (equities, FX, sovereign bonds) in emerging economies. It assesses the extent, evolution and country-level heterogeneity of these transmissions. The study employ a two-stage econometric framework. First, ARIMA–GARCH models filter returns and estimate conditional volatilities. Second, a Time-Varying Parameter Vector Auto-Regression (TVP–VAR) model with Generalized Forecast Error Variance Decomposition (GFEVD) captures the magnitude, direction and temporal evolution of volatility spillovers. The dataset comprises daily data (2015–2023) for Bitcoin, Ethereum and Binance Coin and financial indicators from India, Brazil, Turkey, Indonesia and South Africa. The findings reveal significant, time-varying spillovers from cryptocurrencies to traditional markets, intensifying during bear markets and crises. Cryptocurrencies act as net transmitters of volatility under stress. Spillover intensity varies across countries, with Turkey and India exhibiting the highest exposure due to restrictive policies and regulatory ambiguity, while South Africa's neutral stance results in lower connectedness. This study empirically assess asymmetric and country-specific volatility spillovers from cryptocurrencies to traditional markets using a TVP–VAR–GFEVD framework. The findings contribute to financial contagion theory and offer crucial insights for risk monitoring and regulatory design in emerging markets shaped by digital asset dynamics.