Jeffrey Chu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Jeffrey Chu has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Joerg Osterrieder
University of Twente, Enschede, Netherlands
2 shared publications - Saralees Nadarajah
Department of Mathematics, University of Manchester, Manchester M13 9PL, UK
2 shared publications - Stephen Chan
American University of Sharjah, Sharjah, UAE
2 shared publications
Latest Publications
- Top Cited PapersOpen Access
- 1 October 2017
- Published by MDPI AG in Journal of Risk and Financial Management
- Vol. 10 (4) , 17
GARCH Modelling of Cryptocurrencies
With the exception of Bitcoin, there appears to be little or no literature on GARCH modelling of cryptocurrencies. This paper provides the first GARCH modelling of the seven most popular cryptocurrencies. Twelve GARCH models are fitted to each cryptocurrency, and their fits are assessed in terms of five criteria. Conclusions are drawn on the best fitting models, forecasts and acceptability of value at risk estimates.
- 1 October 2017
- Published by MDPI AG in Journal of Risk and Financial Management
- Vol. 10 (4) , 17
Top Cited PapersOpen AccessGARCH Modelling of Cryptocurrencies
With the exception of Bitcoin, there appears to be little or no literature on GARCH modelling of cryptocurrencies. This paper provides the first GARCH modelling of the seven most popular cryptocurrencies. Twelve GARCH models are fitted to each cryptocurrency, and their fits are assessed in terms of five criteria. Conclusions are drawn on the best fitting models, forecasts and acceptability of value at risk estimates.
- Open Access
- 31 May 2017
- Published by MDPI AG in Journal of Risk and Financial Management
- Vol. 10 (2) , 12
A Statistical Analysis of Cryptocurrencies
We analyze statistical properties of the largest cryptocurrencies (determined by market capitalization), of which Bitcoin is the most prominent example. We characterize their exchange rates versus the U.S. Dollar by fitting parametric distributions to them. It is shown that returns are clearly non-normal, however, no single distribution fits well jointly to all the cryptocurrencies analysed. We find that for the most popular currencies, such as Bitcoin and Litecoin, the generalized hyperbolic distribution gives the best fit, while for the smaller cryptocurrencies the normal inverse Gaussian distribution, generalized t distribution, and Laplace distribution give good fits. The results are important for investment and risk management purposes.
- 31 May 2017
- Published by MDPI AG in Journal of Risk and Financial Management
- Vol. 10 (2) , 12
Open AccessA Statistical Analysis of Cryptocurrencies
We analyze statistical properties of the largest cryptocurrencies (determined by market capitalization), of which Bitcoin is the most prominent example. We characterize their exchange rates versus the U.S. Dollar by fitting parametric distributions to them. It is shown that returns are clearly non-normal, however, no single distribution fits well jointly to all the cryptocurrencies analysed. We find that for the most popular currencies, such as Bitcoin and Litecoin, the generalized hyperbolic distribution gives the best fit, while for the smaller cryptocurrencies the normal inverse Gaussian distribution, generalized t distribution, and Laplace distribution give good fits. The results are important for investment and risk management purposes.