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 JFRM  Vol.8 No.1 , March 2019
Half-Life Volatility Measure of the Returns of Some Cryptocurrencies
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.
Cite this paper: John, A. , Logubayom, A. and Nero, R. (2019) Half-Life Volatility Measure of the Returns of Some Cryptocurrencies. Journal of Financial Risk Management, 8, 15-28. doi: 10.4236/jfrm.2019.81002.
References

[1]   Ali, R., Barrdear, J., Clews, R., & Southgate, J. (2014). The Economics of Digital Currencies. Bank of England Quarterly Bulletin, 54, 276–286.

[2]   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.

[3]   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

[4]   Baur, D. G., Kihoon, H., & Andrian. D. L. (2017). Bitcoin: Medium of Exchange or Speculative Assets?

[5]   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.

[6]   Bradbury, D. (2013). The Problem with Bitcoin. Computer Fraud & Security, 2013, 5-8.
https://doi.org/10.1016/S1361-3723(13)70101-5

[7]   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

[8]   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.

[9]   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

[10]   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

[11]   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

[12]   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

[13]   Koutmos, D. (2018). Bitcoin Returns and Transaction Activity. Economics Letters, 167, 81-85.
https://doi.org/10.1016/j.econlet.2018.03.021

[14]   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

[15]   Li, X., & Wang, C. A. (2017). The Technology and Economic Determinants of Cryptocurrency Exchange Rates. The Case of Bitcoin. Decision Support Systems, 95, 49-60.
https://doi.org/10.1016/j.dss.2016.12.001

[16]   Polasik, M., Piotrowska, A. I., Wisniewski, T. P., Kotkowski, R., & Lightfoot, G. (2015). Price Fluctuations and the Use of Bitcoin: An Empirical Inquiry. International Journal of Electronic Commerce, 20, 9-49. https://doi.org/10.1080/10864415.2016.1061413

[17]   Salisu, A. A., Tiwari A. K., & Raheem I. D. (2018). Analysing the Distribution Properties of Bitcoin Returns. Working Papers Series, CWPS 0058, Ibadan: Centre for Econometric and Allied Research, University of Ibadan.

 
 
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