This study investigates the multivariate topology and determinants of systemic risks within the nascent NFT marketplace across eight bellwether digital assets. It aims to elucidate the complex and dynamic network of risks permeating the NFT ecosystem, generating actionable intelligence surrounding portfolio construction, risk management and governance for investors and regulators navigating this proliferating volatile asset class.
A quantile vector autoregression (QVAR) approach is employed to examine the interconnectedness of NFT assets. Static quantile connectedness matrices, directional connectedness heatmaps and quantile-on-quantile matrices are utilized to unpack the asymmetric, state-dependent spillovers between assets.
Our findings reveal that NFT assets exhibit moderate yet significant interconnectedness in neutral market states, which escalates during extreme conditions, consistent with a heightened susceptibility to event-driven volatility cascades. The directional connectedness heatmaps highlight frequent role reversals between NFT assets acting as transmitters and receivers of volatility, conditional on quantile-specific market states. Furthermore, the quantile-on-quantile matrices unpack asymmetric, state-dependent spillovers between NFT assets, with more pronounced dependencies arising when asset pairs occupy extreme opposite quantiles.
This study offers a novel perspective on NFT market dynamics by employing sophisticated econometric techniques to capture nuanced interactions within this digital asset class. The comprehensive analysis of a large number of NFT assets, the characterization of tail connectedness and the granular examination of asset interrelationships under extreme market conditions represent significant advancements in understanding the complexities of the NFT market. The insights generated are valuable for investors, market analysts and policymakers operating in this emerging market.
