  We model the dynamics of the cryptocurrency (CC) asset class via a stochastic
volatility with correlated jumps (SVCJ) model with rolling-window parameter
estimates. By analyzing the time-series of parameters, stylized patterns are
observable which are robust to changes of the window size and supported by
cluster analysis. During bullish periods, volatility stabilizes at low levels
and the size and volatility of jumps in mean decreases. In bearish periods
though, volatility increases and takes longer to return to its long-run trend.
Furthermore, jumps in mean and jumps in volatility are independent. With the
rise of the CC market in 2017, a level shift of the volatility of volatility
occurred. All codes are available on Quantlet.com.
