Long Memory Interdependency and Inefficiency in Bitcoin Markets
By Eng-Tuck Cheah, T. Mishra, Mamata Parhi, Zhuang Zhang. Economics Letters, 2018
159 citations recorded by the source corpus.
We model cross-market Bitcoin prices as long-memory processes and study dynamic interdependence in a fractionally cointegrated VAR framework. We find long-memory in both the individual markets and the system of markets depicting non-homogeneous informational inefficiency. Moreover, Bitcoin markets are found to be fractionally cointegrated, where uncertainty negatively impacts this type of cointegration relationship.
Subfields: Economics and Econometrics, Information Systems
View Paper: https://www.semanticscholar.org/paper/d41472ccbfd8c658500c35e9b52ea30bedcc1a3a
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Shaen Corbet
- Brian M. Lucey
- Ender Demir
- Paraskevi Katsiampa
- Andrew Urquhart
- L. Yarovaya
- Chi Keung Marco Lau
- Giray Gozgor
In Information Systems
- Shaen Corbet
- Paraskevi Katsiampa
- Brian M. Lucey
- Andrew Urquhart
- Ender Demir
- L. Yarovaya
- Chi Keung Marco Lau
- Giray Gozgor
Citations in the corpus
- Effects of the geopolitical risks on Bitcoin returns and volatility (2019, methodology): “Following Demir et al. (2018) and Katsiampa (2017), the Bitcoin price data is obtained from http://www.”
- High frequency volatility co-movements in cryptocurrency markets (2019, background): “Among few authors who have studied the interconnectedness of cryptocurrency markets are Fry and Cheah [2016], Cheah et al. [2018], Ciaian et al. [2018], Corbet et al. [2018] Katsiampa [2017], Katsiampa [2018] and Katsiampa et al. [2019]. More specifically, Cheah et al. [2018] modelled cross market B”
- Cryptocurrencies as a financial asset: A systematic analysis (2019, methodology): “While investigating the general behavioural aspects of cryptocurrencies, Corbet et al. [2017] examined the reaction of a broad set of digital assets to US Federal Fund interest rates and quantitative easing announcements to find a broad range of differing volatility responses and feedback dependent ”
- Long- and Short-Term Cryptocurrency Volatility Components: A GARCH-MIDAS Analysis (2018, unknown): “For other recent contributions, see Cheah et al. (2018); Khuntia and Pattanayak (2018); and Koutmos (2018).”