2021/03/23 by Konstantin Häusler, Wolfgang Karl Härdle, Häusler, Konstantin +1
Computer Science · Economics, Econometrics and Finance · #62-P05 #91-G15 #Blockchain Technology Applications and Security #Complex Systems and Time Series Analysis #FOS: Economics and business #Financial Markets and Investment Strategies #Statistical Finance (q-fin.ST)
paper · pdf · doi:10.48550/arxiv.2103.12461
openalex publication_date 2021/03/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.