Beschreibung:
The main objective of this study is to investigate tail risk connectedness among six major cryptocurrency markets and determine the extent to which investor sentiment, economic conditions, and economic uncertainty can predict tail risk interconnectedness. Combining the Conditional Autoregressive Value-at-Risk (CAViaR) model with the time-varying parameter vector autoregressive (TVP-VAR) approach shows that the transmission of tail risks among cryptocurrencies changes dynamically over time. During crises and signifcant events, transmission bursts and tail risks change. Based on both in- and out-of-sample forecasts, we fnd that the information contained in investor sentiment, economic conditions, and uncertainty includes signifcant predictive content about the tail risk connectedness of cryptocurrencies.