Jiang, Z.; Mensi, W.; Yoon, S.-M. Risks in Major Cryptocurrency Markets: Modeling the Dual Long Memory Property and Structural Breaks. Sustainability2023, 15, 2193.
Jiang, Z.; Mensi, W.; Yoon, S.-M. Risks in Major Cryptocurrency Markets: Modeling the Dual Long Memory Property and Structural Breaks. Sustainability 2023, 15, 2193.
Jiang, Z.; Mensi, W.; Yoon, S.-M. Risks in Major Cryptocurrency Markets: Modeling the Dual Long Memory Property and Structural Breaks. Sustainability2023, 15, 2193.
Jiang, Z.; Mensi, W.; Yoon, S.-M. Risks in Major Cryptocurrency Markets: Modeling the Dual Long Memory Property and Structural Breaks. Sustainability 2023, 15, 2193.
Abstract
This study estimates the effects of double long memory and structural breaks on the persistence level of six major cryptocurrency markets. We apply the Bai and Perron’s 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 and confirm our hypothesis that ignoring structural breaks leads to an underestimation of the persistence of volatility modelling. The ARFIMA-FIGARCH model with structural breaks and a skewed Student–t distribution fits the cryptocurrency market’s price dynamics well.
Keywords
cryptocurrency; double long memory (LM); structural breaks (SBs); efficient market hypothesis; ARFIMA-FIGARCH model
Subject
Business, Economics and Management, Finance
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
