Central Bank Uncertainty, Investor Sentiment, and Crash Risk in Digital Assets

Abstract

This dissertation contains two essays. The first essay examines the connectedness among central bank digital currency uncertainty (CBDC), investor sentiment, and digital assets using the Quantile Vector Autoregressive connectedness model, following Ando et al. (2022). This analysis uses data from June 2018 to November 2024. The findings show that the connectedness is substantial and varies across market conditions. Spillovers become stronger during extreme states than during normal periods. The results also suggest that the pattern of shock transmission changes across quantiles. Overall, the evidence shows that CBDC and investor sentiment are linked to digital asset market dynamics and play an important role in the propagation of shocks.

The second essay examines the interdependencies among cryptocurrencies and their crash-risk dynamic. The cryptocurrency market is highly volatile, raising concerns about sudden downside risk and crash transmission across digital assets. This study examines crash risk among eight major cryptocurrencies using crash risk measures constructed from 5-minute high-frequency data and the connectedness frameworks of Diebold and Yılmaz (2014) and Baruník and Křehlík (2018). The results show substantial crash risk connectedness across the sample, although the direction and intensity of spillovers differ across assets. The frequency-domain results further show that crash risk spillovers are driven predominantly by short-run dynamics, with much weaker long-run connectedness. Additionally, the COVID-19 period slightly strengthens the long-run component of connectedness and changes the distribution of transmitter and receiver roles across cryptocurrencies. Overall, the findings provide useful insights for investors and policymakers concerned with downside contagion in the cryptocurrency market.