Abstract
This study estimates the effects of the dual long memory property and structural breaks on the persistence level of six major cryptocurrency markets. We apply the Bai and Perron structural break test, Inclán and Tiao’s iterated cumulative sum of squares (ICSS) algorithm, and the fractionally integrated generalized autoregressive conditional heteroscedasticity (FIGARCH) model, with different distributions. The results show that long memory and structural breaks characterize the conditional volatility of cryptocurrency markets, confirming our hypothesis that ignoring structural breaks leads to an underestimation of the persistence of volatility modeling. The ARFIMA-FIGARCH model, with structural breaks and a skewed Student (Formula presented.) distribution, fits the cryptocurrency market’s price dynamics well.
| Original language | English |
|---|---|
| Article number | 2193 |
| Journal | Sustainability (Switzerland) |
| Volume | 15 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - Feb 2023 |
Keywords
- ARFIMA-FIGARCH model
- cryptocurrency
- dual long memory (LM)
- efficient market hypothesis
- structural breaks (SBs)
ASJC Scopus subject areas
- Computer Science (miscellaneous)
- Geography, Planning and Development
- Renewable Energy, Sustainability and the Environment
- Building and Construction
- Environmental Science (miscellaneous)
- Energy Engineering and Power Technology
- Hardware and Architecture
- Computer Networks and Communications
- Management, Monitoring, Policy and Law
