Asymmetric Volatility Spillover between Cryptocurrencies and Global Equity Markets: A TVP-VAR (Time-Varying Parameter Vector Autoregression Approach)
Abstract: This thesis investigates the asymmetric volatility spillover dynamics between major cryptocurrencies and global equity markets using a Time-Varying Parameter Vector Autoregression (TVP-VAR) framework. Employing daily data from January 2018 to December 2024, I analyze seven major cryptocurrencies and six developed equity indices. I decompose volatility into "good" (positive return shocks) and "bad" (negative return shocks) components using realized semivariances and estimate dynamic connectedness networks. The findings reveal three key results: First, volatility spillovers are time-varying and intensify significantly during crisis periods (COVID-19, Russia-Ukraine war, FTX collapse). Second, I document a systematic asymmetry- negative (bad) volatility spillovers are consistently larger than positive (good) spillovers, with the Spillover Asymmetry Measure (SAM) increasing by 40-60% during market stress. Third, cryptocurrencies act as net transmitters of volatility during stress periods but switch to net receivers during stable market conditions. These results have important implications for portfolio diversification, risk management, and regulatory policy in an increasingly interconnected digital asset ecosystem.
Keywords: Volatility Spillovers, Cryptocurrencies, Equity Markets, TVP-VAR, Asymmetric Effects, Dynamic Connectedness
How to Cite?: Dr. Ashok Jahagirdar, "Asymmetric Volatility Spillover between Cryptocurrencies and Global Equity Markets: A TVP-VAR (Time-Varying Parameter Vector Autoregression Approach)", Volume 15 Issue 9, September 2026, International Journal of Science and Research (IJSR), Pages: 1373-1384, https://www.ijsr.net/getabstract.php?paperid=SR26920163911, DOI: https://dx.doi.org/10.21275/SR26920163911