This study explores the impacts of structural breaks (SB) on the dual long memory levels of Bitcoin and Ethereum price returns. We identify dual long memory and structural changes on cryptocurrency markets using four different generalized autoregressive conditional heteroskedasticity models (e.g., GARCH, FIGARCH, FIAPARCH, and HYGARCH). Furthermore, the persistence level of both returns and volatility decreases after accounting for long memory and switching states. Finally, the FIGARCH model with SB variables provides a comparatively superior forecasting accuracy performance. These findings have significant implications for both cryptocurrency allocations and portfolio management.
|Journal||Finance Research Letters|
|Publication status||Accepted/In press - Jan 1 2018|
- GARCH family models
- Long memory
- Structural breaks
ASJC Scopus subject areas