A TIME SERIES ANALYSIS OF FOUR MAJOR CRYPTOCURRENCIES

Boris Radovanov, Aleksandra Marcikić, Nebojša Gvozdenović

DOI Number
https://doi.org/10.22190/FUEO1803271R
First page
271
Last page
278

Abstract


Because of increasing interest in cryptocurrency investments, there is a need to quantify their variation over time. Therefore, in this paper we try to answer a few important questions related to a time series of cryptocurrencies. According to our goals and due to market capitalization, here we discuss the daily market price data of four major cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Ripple (XRP) and Litecoin (LTC). In the first phase, we characterize the daily returns of exchange rates versus the U.S. Dollar by assessing the main statistical properties of them. In many ways, the interpretation of these results could be a crucial point in the investment decision making process. In the following phase, we apply an autocorrelation function in order to find repeating patterns or a random walk of daily returns. Also, the lack of literature on the comparison of cryptocurrency price movements refers to the correlation analysis between the aforementioned data series. These findings are an appropriate base for portfolio management. Finally, the paper conducts an analysis of volatility using dynamic volatility models such as GARCH, GJR and EGARCH. The results confirm that volatility is persistent over time and the asymmetry of volatility is small for daily returns.

Keywords

cryptocurrencies, time series, volatility

Full Text:

PDF

References


Bariviera, A., Basgall, M.J., Hasperue, W. & Naiouf, M. (2017). Some Stylized Facts of the Bitcoin Market. Physica A, 484, 82-90.

Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31 (1), 307-327.

Bouoiyour, J. & Selmi, R. (2016). Bitcoin: A Beginning of a New Phase?, Economics Bulletin, 36 (3), 1430-1440.

Bouri, E., Azzi, G. & Dyhrberg, A.H. (2017). On the Return-Volatility Relationships in the Bitcoin Market around the Price Crach of 2013. Economics: The Open-Access, Onep-Assessement E Journal, 11 (1), pp.1-16.

Briere, M., Oosterlinck, K. & Szafarz, A. (2015). Virtual Currency, Tangible Return: Portfolio Diversification with Bitcoins. Journal of Asset Management, 16 (1), 365-373.

Catania, L., Grassi, S. & Ravazzolo, F. (2018). Predicting the Volatility of Cryptocurrency Time Series. (Oslo: CAMP Working Paper Series No 5/2018.) Retrieved from: http://brage.bibsys.no/bitstream/handle/11250/2489408/WP_CAMP_5_2018.pdf Accessed on: 15 January 2018.

Chen, J., Hong, H. & Stein, J. (2001). Forecasting Crashes: Trading Volume, Past Returns and Conditional Skewness in Stock Prices. Journal of Financial Economics, 61 (3), 345-381.

Chan, S., Chu, J., Nadarajah, S. & Oserrieder, J. (2017). A Statistical Analysis of Cryptocurrencies. Journal of Risk and Financial Management, 10 (12), 1-23.

Cheah, E.T. & Fry, J. (2015). Speculative Bubbles in Bitcoin Markets? An Emprical Investigation into the Fundamental Value of Bitcoin. Economic Letters, 130 (1), 32-36.

CoinMarketCap, (2018). Cryptocurrency Market Capitalizations. Retrieved from: http://coinmarketcap.com/coins/views/all/ Accessed on: 8 March 2018.

Chu, J.. Chan, S., Nadarajah, S. & Osterrieder, J. (2017). GARCH Modelling of Cryptocurrencies. Journal of Risk and Financial Management, 10 (17), 1-15.

Dyhrberg, A.H. (2016). Hedging Capabilities of Bitcoin. Is It the Virtual Gold? Finance Research Letters, 16 (1), 139-144.

Glosten, L., Jagannathan, R. & Runkle, D. (1993). On the Relation Between the Expected Value and the Volatility of the Nominal Excess Return on Stocks. Journal of Finance, 48 (1), 1779-1807.

Hegadekatti, K. (2017). The K-Y Paradox: Problems in Creating a Centralised Sovereign Backed Cryptocurrency on a Decentralised Platform. Retrieved from: https://ssrn.com/abstract=2942914 Accessed on: 30 April 2018.

Katsiampa, P. (2017). Volatility Estimation for Bitcoin: A Comparison of GARCH Models. Economics Letters, 158, 3-6.

Kristoufek, L. (2015). What are the Main Drivers of the Bitcoin Price? Evidence from Wavelet Coherence Analysis. PLoS ONE 10. Retrieved from: journals.plos.org/plosone/article?id=10.1371/journal.pone.0123923 Accessed on: 9 March 2018.

Nakamoto, S. (2008). A Peer-to-Peer Electronic Cash System. Retrieved from: http://bitcoin.org/bitcoin.pdf Accessed on 8 March 2018.

Nelson, D. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59 (1), 347-370.

Pinna, A. & Ruttenberg , W. (2016) Distributed ledger technologies in securities post-trading. Retrieved from: https://bravenewcoin.com/assets/Industry-Reports-2016/European-Central-Bank-Distributed-ledger-technologies-Report.pdf Accessed on: 17 February 2018.

Radovanov, B. & Marcikić, A. (2017). Bootstrap Testing of Trading Strategies in Emerging Balkan Stock Markets. Ekonomie a Management, 20 (4), 103-119.

Takaishi, T. (2017). Statistical Properties and Multifractality of Bitcoin. Cornel University Library. Retrieved from: http://arxiv.org/abs/1707.07618 Accessed on: 15 January 2018.




DOI: https://doi.org/10.22190/FUEO1803271R

Refbacks

  • There are currently no refbacks.


ISSN 0354-4699 (Print)

ISSN 2406-050X (Online)