Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2021 007

Rodeo or Ascot: which hat to wear at the crypto race?

Konstantin Häusler
Wolfgang Karl Härdle

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.

Cryptocurrency, SVCJ, Market Dynamics, Stochastic Volatility

JEL Classification:
C51, C58, G15