Research ArticleOpen AccessGoogle Scholar indexed
Half-Life Volatility Measure of the Returns of Some Cryptocurrencies
Department of Statistics, Faculty of Mathematical Sciences, University for Development Studies, Tamale, Ghana
Department of Statistics, Faculty of Mathematical Sciences, University for Development Studies, Tamale, Ghana
Department of Mathematics, College of Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
- 1 Department of Statistics, Faculty of Mathematical Sciences, University for Development Studies, Tamale, Ghana
- 2 Department of Statistics, Faculty of Mathematical Sciences, University for Development Studies, Tamale, Ghana
- 3 Department of Mathematics, College of Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
Journal of Financial Risk Management·Volume 08 (2019)·Pages 15–28·Published 15 February 2019·DOI10.4236/jfrm.2019.81002
Copy link · social · email
Abstract
This paper explores the half-life volatility measure of three cryptocurrencies (Bitcoin, Litecoin and Ripple). Two GARCH family models were used (PGARCH (1, 1) and GARCH (1, 1)) with the student-t distribution. It was realised that, the PGARCH (1, 1) was the most appropriate model. Therefore, it was used in determining the half-life of the three returns series. The results revealed that, the half-life was 3 days, 6 days and 4 days for Bitcoin, Litecoin and Ripple respectively. This shows that, the three coins have strong mean reversion and short half-life and that it takes the respective days for volatility in each of coin to return half way back without further volatility.
KeywordsHalf-LifeCryptocurrenciesVolatilityMean ReversionGARCH
- Ali, R., Barrdear, J., Clews, R., & Southgate, J. (2014). The Economics of Digital Currencies. Bank of England Quarterly Bulletin, 54, 276–286.
- Al-Khazali, O., Bouri, E., & Roubaud, D. (2018). The Impact of Positive and Negative Macroeconomic News Surprises: Gold versus Bitcoin. Economics Bulletin, 38, 373-382.
- Balcilar, M., Bouri, E., Gupta, R., & Roubaud, D. (2017). Can Volume Predict Bitcoin Returns and Volatility? A Quantiles-Based Approach. Economic Modelling, 64, 74-81. https://doi.org/10.1016/j.econmod.2017.03.019
- Baur, D. G., Kihoon, H., & Andrian. D. L. (2017). Bitcoin: Medium of Exchange or Speculative Assets?
- Bouri, E., Azzi, G., & Haubo Dyhrberg, A. (2017). On the Return-Volatility Relationship in the Bitcoin Market around the Price Cash of 2013. Economics, 11, 1-16.
- Bradbury, D. (2013). The Problem with Bitcoin. Computer Fraud & Security, 2013, 5-8. https://doi.org/10.1016/S1361-3723(13)70101-5
- Cheah, E.-T., Mishraa, T., Parhi, M., & Zhang, Z. (2018). Long Memory Interdependency and Inefficiency in Bitcoin Markets. Economics Letters, 167, 18-25. https://doi.org/10.1016/j.econlet.2018.02.010
- Conrad, C., & Kleen, O. (2018). Two Are Better Than One: Volatility Forecasting Using Multiplicative Component GARCH Models. Journal of Risk and Financial Management, 23, 1-51.
- Dehrberg, A. H. (2016). Bitcoin, Gold and the Dollar—A GARCH Volatility Analysis. Finance Research Letters, 16, 85-92. https://doi.org/10.1016/j.frl.2015.10.008
- Engle, R. F., & Patton, A. J. (2001). What Good Is a Volatility Model? Quantitative Finance, 1, 237–245. https://doi.org/10.1088/1469-7688/1/2/305
- Katsiampa, P. (2017). Volatility Estimation for Bitcoin: A Comparison of GARCH Models. Economics Letters, 158, 3-6. https://doi.org/10.1016/j.econlet.2017.06.023
- Khuntia, S., & Paltanayak, J. K. (2018). Adaptive Market Hypothesis and Evolving Predictability of Bitcoin. Economics Letters, 167, 26-28. https://doi.org/10.1016/j.econlet.2018.03.005
- Koutmos, D. (2018). Bitcoin Returns and Transaction Activity. Economics Letters, 167, 81-85. https://doi.org/10.1016/j.econlet.2018.03.021
- Kristoufek, L. (2015). What Are the Main Drivers of the Bitcoin Price? Evidence from Wavelet Coherence Analysis. PLoS ONE, 10, e0123923. https://doi.org/10.1371/journal.pone.0123923