Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series", Berlin
This paper sheds light on the dynamics of the cryptocurrency (CC) sector. By modeling its dynamics via a stochastic volatility with correlated jumps (SVCJ) model in combination with several rolling windows, it is possible to capture the extreme ups and downs of the CC market and to understand its dynamics. Through this approach, we obtain time series for each parameter of the model. Even though parameter estimates change over time and depend on the window size, several recurring patterns are observable which are robust to changes of the window size and supported by clustering of parameter estimates: 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.