Evolution of bitcoin as a Financial Asset

The cryptocurrency market debate resumed in 2020 with renewed vigour as the price of Bitcoin surpassed late 2017 highs. This study aims to analyse possible factors of Bitcoin’s pricing at various cryptocurrency market development stages — before the 2017 price bubble, after and during the COVID-19 pandemic. The main method of analysis is a generalized autoregressive conditional heteroskedasticity model with conditional generalized error distribution (GARCHGED). Two groups of indicators are used as possible factors related to the Bitcoin dynamics. The first group consists of various quantitative indicators directly related to Bitcoin (the so-called internal factors) — the volume of exchange trade, the volume of transactions in the Bitcoin blockchain, the number of new and active wallets, hash rate, the sum of fees paid in the blockchain, as well as the dynamics of Google Trends search queries. The second group is the return on various financial assets — stock and bond indexes, commodities, and currency markets. The results of the analysis demonstrate the absence of a stable correlation between any of the factors under consideration and Bitcoin returns in all the periods that we focus on. In the period before the 2017 price bubble, the internal factors and Bitcoin returns showed generally co-directional dynamics, but the situation changed in 2018. In early 2021, the correlation between Bitcoin and traditional financial assets returns has increased significantly. We can conclude that Bitcoin is becoming a popular means of diversification as a high-risk asset, which, however, follows the pattern of a speculative bubble at the beginning of 2021. The increased demand for the need to invest in Bitcoin using various exchange-traded instruments (ETFs for cryptocurrencies) may soon lead to a further increase in the price of this cryptocurrency if such instruments are registered on the exchange.

1. Stolbov M.I. The 10th anniversary of the cryptocurrency market: Its current state and prospects. Voprosy ekonomiki. 2019;(5):136–148. (In Russ.). DOI: 10.32609/0042–8736–2019–5–136–148

2. Shilov K.D., Zubarev A.V. Blockchain and distributed ledgers as a type of database. Innovatsii = Innovations. 2018;12:77–87. (In Russ.).

3. Sinel’nikova-Muryleva E.V., Shilov K. D., Zubarev A.V. The essence of cryptocurrencies: Descriptive and comparative analysis. Finansy: teoriya i praktika = Finance: Theory and Practice. 2019;23(6):36–49. (In Russ.). DOI: 10.26794/2587–5671–2019–23–6–36–49

4. Sockin M., Xiong W. A model of cryptocurrencies. NBER Working Paper. 2020;(26816). DOI: 10.3386/w26816

5. Gans J. S., Halaburda H. Some economics of private digital currency. In: Goldfarb A., Greenstein S. M., Tucker C. E., eds. Economic analysis of the digital economy. Chicago, IL: University of Chicago Press; 2015:257–276. DOI: 10.7208/chicago/9780226206981.003.0009

6. Biais B., Bisiere C., Bouvard M., Casamatta C., Menkveld A.J. Equilibrium Bitcoin pricing. SSRN Electronic Journal. 2020. DOI: 10.2139/ssrn.3261063

7. Cong L.W., Li Y., Wang N. Tokenomics: Dynamic adoption and valuation. The Review of Financial Studies. 2021;34(3):1105–1155. DOI: 10.1093/rfs/hhaa089

8. Chiu J., Koeppl T.V. The economics of cryptocurrencies — Bitcoin and beyond. SSRN Electronic Journal. 2017. DOI: 10.2139/ssrn3048124

9. Hayes A.S. Cryptocurrency value formation: An empirical study leading to a cost of production model for valuing bitcoin. Telematics and Informatics 2017;34(7):1308–1321. DOI: 10.1016/j.tele.2016.05.005

10. Catalini C., Gans J.S. Initial coin offerings and the value of crypto tokens. NBER Working Paper. 2018;(24418). DOI: 10.3386/w24418

11. Chod J., Lyandres E. A theory of ICOs: Diversification, agency, and information asymmetry. Management Science. 2021. In press. DOI: 10.1287/mnsc.2020.3754

12. Simonov А., Zyamalov V. Long run return and survival factors of ICO. Ekonomicheskii zhurnal Vysshei shkoly ekonomiki = The HSE Economic Journal. 2019;23(4):585–604. (In Russ.). DOI: 10.17323/1813–8691–2019–23–4–585–604

13. Dyhrberg A. H. Bitcoin, gold and the dollar — A GARCH volatility analysis. Finance Research Letters. 2016;16:85–92. DOI: 10.1016/j.frl.2015.10.008

14. Baur D.G., Dimpfl T., Kuck K. Bitcoin, gold and the US dollar — A replication and extension. Finance Research Letters. 2018;25:103–110. DOI: 10.1016/j.frl.2017.10.012

15. Klein T., Pham Thu H., Walther T. Bitcoin is not the New Gold — A comparison of volatility, correlation, and portfolio performance. International Review of Financial Analysis. 2018;59:105–116. DOI: 10.1016/j.irfa.2018.07.010

16. Shahzad S. J.H., Bouri E., Roubaud D., Kristoufek L., Lucey B. Is Bitcoin a better safe-haven investment than gold and commodities? International Review of Financial Analysis. 2019;63:322–330. DOI: 10.1016/j.irfa.2019.01.002

17. Urquhart A., Zhang H. Is Bitcoin a hedge or safe haven for currencies? An intraday analysis. International Review of Financial Analysis. 2019;63:49–57. DOI: 10.1016/j.irfa.2019.02.009

18. Shahzad S.J.H., Balli F., Naeem M.A., Hasan M., Arif M. Do conventional currencies hedge cryptocurrencies? The Quarterly Review of Economics and Finance. 2021. In press. DOI: 10.1016/j.qref.2021.01.008

19. Glaser F., Zimmermann K., Haferkorn M., Weber M. C., Siering M. Bitcoin — Asset or currency? Revealing users’ hidden intentions. In: Proc. 22nd European conf. on information systems (ECIS-2014). (Tel Aviv, June 9–11, 2014). Frankfurt am Main: Goethe University; 2014. URL: https://www.researchgate.net/publication/286338705_Bitcoin_-_Asset_or_currency_Revealing_users’_hidden_intentions

20. Baur D.G., Hong K., Lee A.D. Bitcoin: Medium of exchange or speculative assets? Journal of International Financial Markets, Institutions and Money. 2018;54:177–189. DOI: 10.1016/j.intfin.2017.12.004

21. Krylov G. O., Lisitsyn A. Yu., Polyakov L. I. Comparative analysis of volatility of cryptocurrencies and fiat money. Finansy: teoriya i praktika = Finance: Theory and Practice. 2018;22(2):66–89. (In Russ.). DOI: 10.26794/2587–5671–2018–22–2–66–89

22. Liu Y., Tsyvinski A. Risks and returns of cryptocurrency. The Review of Financial Studies. 2021;34(6):2689–2727. DOI: 10.1093/rfs/hhaa113

23. Dastgir S., Demir E., Downing G., Gozgor G., Lau C.K.M. The causal relationship between Bitcoin attention and Bitcoin returns: Evidence from the Copula-based Granger causality test. Finance Research Letters. 2019;28:160–164. DOI: 10.1016/j.frl.2018.04.019

24. Liu Y., Tsyvinski A., Wu X. Common risk factors in cryptocurrency. NBER Working Paper. 2019;(25882). DOI: 10.3386/w25882

25. Chu J., Chan S., Nadarajah S., Osterrieder J. GARCH modelling of cryptocurrencies. Journal of Risk and Financial Management. 2017;10(4):17. DOI: 10.3390/jrfm10040017

26. Malkina М. Yu., Ovchinnikov V. N. Cryptocurrency market: Overreaction to news and herd instincts. Ekonomicheskaya politika = Economic Policy. 2020;15(3):74–105. (In Russ.). DOI: 10.18288/1994–5124–2020–3–74–105

27. Fedorova E.A., Bechvaya K. Z., Rogov O. Yu. The influence of the tonality of news on the exchange rate of Bitcoin. Finansy: teoriya i praktika = Finance: Theory and Practice. 2018;22(4):104–113. (In Russ.). DOI: 10.26794/2587–5671–2018–22–4–104–113

28. Aalborg H.A., Molnár P., de Vries J.E. What can explain the price, volatility and trading volume of Bitcoin? Finance Research Letters. 2019;29:255–265. DOI: 10.1016/j.frl.2018.08.010

29. Bianchi D. Cryptocurrencies as an asset class? An empirical assessment. The Journal of Alternative Investments. 2020;23(2):162–179. DOI: 10.3905/jai.2020.1.105

30. Bianchi D., Dickerson A. Trading volume in cryptocurrency markets. SSRN Electronic Journal. 2019. DOI: 10.2139/ssrn.3239670

31. Alexander C., Dakos M. A critical investigation of cryptocurrency data and analysis. Quantitative Finance. 2020;20(2):173–188. DOI: 10.1080/14697688.2019.1641347

32. Enders W. Modeling volatility. In: Enders W. Applied econometric time series. 4th ed. Hoboken, NJ: John Wiley & Sons, Inc.; 2015:118–180.

33. Fama E. F. The behavior of stock-market prices. The Journal of Business. 1965;38(1):34–105. URL: http://www.e-m-h.org/Fama65.pdf

34. Mandelbrot B. B. The variation of certain speculative prices. In: Mandelbrot B. B. Fractals and scaling in finance: Discontinuity, concentration, risk. New York: Springer-Verlag; 1997:371–418.

35. Nelson D.B. Conditional heteroskedasticity in asset returns: A new approach. Econometrica. 1991;59(2):347–370. DOI: 10.2307/2938260

36. Curto J.D., Pinto J.C., Tavares G.N. Modeling stock markets’ volatility using GARCH models with Normal, Student’s t and stable Paretian distributions. Statistical Papers. 2009;50(2):311–321. DOI: 10.1007/s00362–007–0080–5

37. Alberg D., Shalit H., Yosef R. Estimating stock market volatility using asymmetric GARCH models. Applied Financial Economics. 2008;18(15):1201–1208. DOI: 10.1080/09603100701604225

38. Fisher T. J., Gallagher C. M. New weighted portmanteau statistics for time series goodness of fit testing. Journal of the American Statistical Association. 2012;107(498):777–787. DOI: 10.1080/01621459.2012.688465

39. Ghalanos A. Package ‘rugarch’: Univariate GARCH models. R package version 1.4–4. 2020. URL: https://cran.rproject.org/web/packages/rugarch/rugarch.pdf

40. Vlaar P. J.G., Palm F. C. The message in weekly exchange rates in the European monetary system: Mean reversion, conditional heteroscedasticity, and jumps. Journal of Business & Economic Statistics. 1993;11(3):351–360. DOI: 10.1080/07350015.1993.10509963

41. Andersen T. G. Return volatility and trading volume: An information flow interpretation of stochastic volatility. The Journal of Finance. 1996;51(1):169–204. DOI: 10.1111/j.1540–6261.1996.tb05206.x

42. Fantazzini D., Kolodin N. Does the hashrate affect the Bitcoin price? Journal of Risk and Financial Management. 2020;13(11):263. DOI: 10.3390/jrfm13110263

43. Shanaev S., Sharma S., Shuraeva A., Ghimire B. The marginal cost of mining, Metcalfe’s law and cryptocurrency value formation: Causal inferences from the instrumental variable approach. SSRN Electronic Journal. 2019. DOI: 10.2139/ssrn.3432431