Abstract
ABSTRACT This paper aims to study the impacts of long memory in conditional volatility and conditional non-normality on market risks in Bitcoin and some other cryptocurrencies using an Autoregressive Fractionally Integrated GARCH model with non-normal innovations. Two tail-based risk metrics, namely Value at Risk (VaR) and Expected Shortfall (ES), are adopted to study the tail behaviour of market risks in Bitcoin and some other cryptocurrencies. Empirical investigations for the tail behaviour based on real exchange rate data of cryptocurrencies are conducted. An extreme-value-theory-based approach is used to study potential improvements in the estimation for the risk metrics under GARCH-type models. The possibility of explosive regimes in cryptocurrencies’ volatilities is examined using Markov-switching GARCH models.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
