Stylized facts, volatility dynamics and risk measures of cryptocurrencies
This study explores the stylized facts, volatility clustering, other highly irregular behaviour, and risk measures of cryptocurrencies’ returns. By analysing bitcoin, ripple, and ethereum daily data we establish evidence of strong dependencies among analysed cryptocurrencies. This paper provides new insights about cryptocurrency behaviour and the main measures of risk and detailed comparative analysis with tech-stocks. Comprehensive research on stylized facts confirmed high risk for both cryptocurrencies and tech-stocks with cryptocurrencies being even riskier. Empirical research findings are useful in developing dependence and risk strategies for investment and hedging purposes, especially during more volatile periods in the markets as there was confirmed existence of volatility clusters when high volatility periods are followed by low volatility periods. Sensitivity analysis and measures of Value-at-Risk (VaR) and Expected Shortfall (ES) show the amount of losses investors can expect in the worst case scenario. Our results confirm the existence of predictability, volatility clustering, and possibilities for arbitrage opportunities. Findings could be beneficial for investors and policymakers as well as for scientific purposes as findings give us a better understanding of the behaviour of cryptocurrencies.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Almeida, D., Dionísio, A., Vieira, I., & Ferreira, P. (2022). Uncertainty and risk in the cryptocurrency market. Journal of Risk and Financial Management, 15(11), 532. https://doi.org/10.3390/jrfm15110532
Almeida, J., & Gonçalves, T. C. (2022). Portfolio diversification, hedge and safe-haven properties in cryptocurrency investments and financial economics: A systematic literature review. Journal of Risk and Financial Management, 16(1), 3. https://doi.org/10.3390/jrfm16010003
Artzner, P., Delbaen, F., Jean-Marc, E. & Heath, D. (1999). Coherent measures of risk. Mathematical Finance, 9, 203–228. https://doi.org/10.1111/1467-9965.00068
Bariviera, A. F. (2017). The inefficiency of Bitcoin revisited: A dynamic approach. Economics Letters, 161, 1–4. https://doi.org/10.1016/j.econlet.2017.09.013
Beneki, C., Koulis, A., Kyriazis, N. A., & Papadamou, S. (2019). Investigating volatility transmission and hedging properties between Bitcoin and Ethereum. Research in International Business and Finance, 48, 219–227. https://doi.org/10.1016/j.ribaf.2019.01.001
Borri, N. (2019). Conditional tail-risk in cryptocurrency markets. Journal of Empirical Finance, 50, 1–19. https://doi.org/10.1016/j.jempfin.2018.11.002
Bruzgė, R., & Šapkauskienė, A. (2022). Network analysis on Bitcoin arbitrage opportunities. The North American Journal of Economics and Finance, 59, 101562. https://doi.org/10.1016/j.najef.2021.101562
Bruzgė, R. (2023). Appendix. Mendeley Data, V1. https://doi.org/10.17632/22xmtknw62.1
Canh, N. P., Wongchoti, U., Thanh, S. D., & Thong, N. T. (2019). Systematic risk in cryptocurrency market: Evidence from DCC-MGARCH model. Finance Research Letters, 29, 90–100. https://doi.org/10.1016/j.frl.2019.03.011
Chaim, P., & Laurini, M. P. (2019). Nonlinear dependence in cryptocurrency markets. North American Journal of Economics and Finance, 48, 32–47. https://doi.org/10.1016/j.najef.2019.01.015
Chu, J., Chan, S., & Zhang, Y. (2021). Bitcoin versus high-performance technology stocks in diversifying against global stock market indices. Physica A: Statistical Mechanics and its Applications, 580, 126161. https://doi.org/10.1016/j.physa.2021.126161
Chuen, Lee, D. K., Guo, L., & Wang, Y. (2018). Cryptocurrency: A new investment opportunity? Journal of Alternative Investments, 20(3), 16–40. https://doi.org/10.3905/jai.2018.20.3.016
Corbet, S., Meegan, A., Larkin, C., Lucey, B., & Yarovaya, L. (2018). Exploring the dynamic relationships between cryptocurrencies and other financial assets. Economics Letters, 165, 28–34. https://doi.org/10.1016/j.econlet.2018.01.004
De Pace, P. & Rao, J. (2022). Comovement and instability in cryptocurrency markets. International Review of Economics & Finance, 83(1). https://doi.org/10.2139/ssrn.3523993
Elendner, H., Trimborn, S., Ong, B. & Ming, T. (2016). The cross-section of crypto-currencies as financial assets: An overview. Investing in crypto-currencies beyond Bitcoin. In Handbook of blockchain, digital finance and inclusion (Vol. 1, pp.145–173). Elsevier.
Fakhfekh, M., & Jeribi, A. (2019). Volatility dynamics of crypto-currencies’ returns: Evidence from asymmetric and long memory GARCH models. Research in International Business and Finance, 51, 101075. https://doi.org/10.1016/j.ribaf.2019.101075
Gkillas, K., & Katsiampa, P. (2018). An application of extreme value theory to cryptocurrencies. Economics Letters, 164, 109–111. https://doi.org/10.1016/j.econlet.2018.01.020
Gyamerah, S. A. (2019). Modelling the volatility of Bitcoin returns using GARCH models. Quantitative Finance and Economics, 3, 739–753. https://doi.org/10.3934/QFE.2019.4.739
Görgen, K., Meirer, J., & Schienle, M. (2022). Predicting value at risk for cryptocurrencies using generalized random forests. arXiv. https://doi.org/10.48550/arXiv.2203.08224
Hrytsiuk, P., Babych, T., & Bachyshyna, L. (2019). Cryptocurrency portfolio optimization using value-at-risk measure. Advances in economics, Business and Management Research, 95, 385–389. https://doi.org/10.2991/smtesm-19.2019.75
Yamai, Y., & Yoshiba, T. (2005). Value-at-risk versus expected shortfall: A practical perspective. Journal of Banking and Finance, 29(4), 997–1015. https://doi.org/10.1016/j.jbankfin.2004.08.010
Jiang, Y., Nie, H., & Ruan, W. (2018). Time-varying long-term memory in Bitcoin market. Finance Research Letters, 25, 280–284. https://doi.org/10.1016/j.frl.2017.12.009
Jiang, K., Zeng, L., Song, J., & Liu, Y. (2022). Forecasting Value-at-Risk of cryptocurrencies using the time-varying mixture-accelerating generalized autoregressive score model. Research in International Business and Finance, 61, 101634. https://doi.org/10.1016/j.ribaf.2022.101634
Ji, Q., Bouri, E., Lau, C. K. M., & Roubaud, D. (2019). Dynamic connectedness and integration in cryptocurrency markets. International Review of Financial Analysis, 63, 257–272. https://doi.org/10.1016/j.irfa.2018.12.002
Le, L., Abakah, E. J., & Tiwari, A. K. (2021). Time and frequency domain connectedness and spill-over among fintech, green bonds and cryptocurrencies in the age of the fourth industrial revolution. Technological Forecasting and Social Change, 162. https://doi.org/10.1016/j.techfore.2020.120382
Likitratcharoen, D., Ranong, T. N., Chuengsuksomboon, R., Sritanee, N., & Pansriwong, A. (2018). Value at risk performance in cryptocurrencies. The Journal of Risk Management and Insurance, 22(1), 11–28.
Makarov, I., & Schoar, A. (2020). Trading and arbitrage in cryptocurrency markets. Journal of Financial Economics, 135(2), 293–319. https://doi.org/10.1016/j.jfineco.2019.07.001
Makrichoriti, P. K., & Moratis, G. (2016). BitCoin’s roller coaster: systemic risk and market sentiment. SSRN. https://doi.org/10.2139/ssrn.2808096
McNeil, A. J., Frey, R., & Embrechts, P. (2015). Quantitative risk management: Concepts, techniques, and tools. Princeton University Press.
Melki, A., & Nefzi, N. (2022). Tracking safe haven properties of cryptocurrencies during the COVID-19 pandemic: A smooth transition approach. Finance Research Letters, 46, 102243. https://doi.org/10.1016/j.frl.2021.102243
Müller, F. M., Santos, S. S., Gössling, T. W., & Righi, M. B. (2022). Comparison of risk forecasts for cryptocurrencies: A focus on Range Value at Risk. Finance Research Letters, 48, 102916. https://doi.org/10.1007/s10614-022-10330-x
Omane-Adjepong, M., Ababio, K. A., & Alagidede, I. P. (2019). Time-frequency analysis of behaviourally classified financial asset markets. Research in International Business and Finance, 50, 54–69. https://doi.org/10.1016/j.ribaf.2019.04.012
Pele, D. T., & Mazurencu-Marinescu-Pele, M. (2019). Using high-frequency entropy to forecast Bitcoin’s daily value at risk. Entropy, 21(2), 102. https://doi.org/10.3390/e21020102
Polasik, M., Piotrowska, A. I., Wisniewski, T. P., Kotwoski, R., & Lightfoot, G. (2016). Price fluctuations and the use of Bitcoin: an empirical inquiry. International Journal of Electronic Commerce, 20(1), 9–49. https://doi.org/10.1080/10864415.2016.1061413
Salisu, A. A., Isah, K., & Akanni, L. O. (2019). Improving the predictability of stock returns with Bitcoin prices. North American Journal of Economics and Finance, 48, 857–867. https://doi.org/10.1016/j.najef.2018.08.010
Sifat, I. M., Mohamad, A., & Mohamed Shariff, M. S. B. (2019). Lead-Lag relationship between Bitcoin and Ethereum: Evidence from hourly and daily data. Research in International Business and Finance, 50, 306–321. https://doi.org/10.1016/j.ribaf.2019.06.012
Symitsi, E., & Chalvatzis, K. J. (2019). The economic value of Bitcoin: A portfolio analysis of currencies, gold, oil and stocks. Research in International Business and Finance, 48, 97–110. https://doi.org/10.1016/j.ribaf.2018.12.001
Tran, V. L., & Leirvik, T. (2020). Efficiency in the markets of crypto-currencies. Finance Research Letters, 35. https://doi.org/10.1016/j.frl.2019.101382
Thaqeb, S. A., & Algharabali, B. G. (2019). Economic policy uncertainty: A literature review. The Journal of Economic Asymmetries, 20. https://doi.org/10.1016/j.jeca.2019.e00133
Trucíos, C., Tiwari, A. K., & Alqahtani, F. (2019). Value-at-risk and expected shortfall in cryptocurrencies’ portfolio: A vine copula–based approach. Applied Economics, 52(24), 2580–2593. https://doi.org/10.1080/00036846.2019.1693023
Katsiampa, P., Yarovaya, L., & Zięba, D. (2022). High-frequency connectedness between Bitcoin and other top-traded crypto assets during the COVID-19 crisis. Journal of International Financial Markets, Institutions and Money, 79, 1042–4431. https://doi.org/10.1016/j.intfin.2022.101578
Urquhart, A. (2017). Price clustering in Bitcoin. Economics Letters, 159, 145–148. https://doi.org/10.1016/j.econlet.2017.07.035
Vaitonis, M., & Masteika, S. (2021). A method for testing high frequency statistical arbitrage trading strategies in electronic exchanges. Transformations in Business & Economics, 20(2B(53B)), 1024–1052.
Qian, L., Wang, J., Ma, F., & Li, Z. (2022). Bitcoin volatility predictability – The role of jumps and regimes. Finance Research Letters, 47, 102687. https://doi.org/10.1016/j.frl.2022.102687
Wang, H., Wang, X., Yin, S., & Ji, H., (2022). The asymmetric contagion effect between stock market and cryptocurrency market. Finance Research Letters, 46(Part A), 102345. https://doi.org/10.1016/j.frl.2021.102345