Journal of Applied Economic Research
ISSN 2712-7435
Investment Behavior in the Global Cryptocurrency Market: Do Traders Take into Account the Possibilities of Diversification?
Anastasiia A. Dergileva, Victoria V. Dobrynskaya, Sergei V. Gurov, Tatiana V. Sokolova
National Research University Higher School of Economics, Moscow, Russia
Abstract
The cryptocurrency market has been considered until recently a market for private, non-portfolio investors, and the research focus has been on volatility. However, gradual institutionalization and creation of ETFs have highlighted new characteristics, including the asymmetry of trade behavior. Our research aims to analyze the relationships between asymmetric risk indicators and the returns of portfolios across a wide range of cryptocurrencies. We test the hypothesis of the significance of skewness and systematic skewness (coskewness) and their premiums for investors in the cryptocurrency market. Various alternatives for building a cryptocurrency portfolio are tested against two market benchmarks: the stock index and the cryptocurrency index. The Fama–Macbeth approach is used with Newey–West standard error correction. The sample is based on weekly data from 2010 to 2023. The calculations, both for the entire time period under consideration and for two sub-periods (before and after the beginning of the COVID-19 crisis) show that systematic skewness and beta of cryptocurrency portfolios remain statistically insignificant and unstable in terms of signs. Thus, the co-movement with the stock market does not have a significant explanatory power for future returns (i.e., returns over the next week). We reveal the unique behavior of investors in the cryptocurrency market: the skewness of returns is a significant factor in the pricing of cryptocurrencies, while systematic skewness is not. The theoretical significance of our study is related to the identification of anomalies in the global cryptocurrency market. Unlike the stock market, we show that there is no systematic skewness premium in the global cryptocurrency market. The methodology reveals the behaviour of investors: they do not take into account the dynamics of the stock market when choosing cryptocurrencies, and therefore, there is no such strategy of diversifying capital to reduce risk. The practical significance is that our findings can serve as a basis for private and institutional investors and asset managers to make investment decisions in the cryptocurrency market.
Keywords
cryptocurrency market; skewness; systematic skewness; CAPM model with third moment; Fama–Macbeth method; investment portfolios
JEL classification
G11, G12References
1. Corbet, S., Lucey, B., Urquhart, A., Yarovaya, L. (2018). Cryptocurrencies as a financial asset: A systematic analysis. International Review of Financial Analysis, Vol. 62, 182–199. https://doi.org/10.1016/j.irfa.2018.09.003
2. Liu, Y., Chen, Y. (2024). Skewness risk and the cross-section of cryptocurrency returns. International Review of Financial Analysis, Vol. 96, Part A, 103626. https://doi.org/10.1016/j.irfa.2024.103626
3. Li, T., Shin, D., Wang, B. (2021). Cryptocurrency pump-and-dump schemes. SSRN Electronic Journal, 3267041. http://dx.doi.org/10.2139/ssrn.3267041
4. Fung, K., Jeong, J., Pereira, J. (2021). More to cryptos than Bitcoin: A GARCH modelling of heterogeneous cryptocurrencies. Finance Research Letters, Vol. 47, Part A, 102544. https://doi.org/10.1016/j.frl.2021.102544
5. Bruhn, P., Ernst, D. (2022). Assessing the risk characteristics of the cryptocurrency market: A GARCH-EVT-Copula approach. Journal of Risk and Financial Management, Vol. 15, Issue 8, 346. https://doi.org/10.3390/jrfm15080346
6. Gupta, H., Chaudhary, R. (2022). An empirical study of volatility in cryptocurrency market. Journal of Risk and Financial Management, Vol. 15, Issue 11, 513. https://doi.org/10.3390/jrfm15110513
7. Lahiani, A., Jeribi, A., Jlassi, N.B. (2020). Nonlinear tail dependence in cryptocurrency–stock market returns: The role of Bitcoin futures. Research in International Business and Finance, Vol. 56, 101351. https://doi.org/10.1016/j.ribaf.2020.101351
8. Liu, Y. (2024). Applicability analysis of cryptocurrency market based on capital asset pricing model. Highlights in Business Economics and Management, Vol. 24, 923–928. https://doi.org/10.54097/dkzd7p22
9. Kahneman, D., Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, Vol. 47, No. 2, 263–292. https://doi.org/10.2307/1914185
10. Benartzi, S., Thaler, R.H. (1995). Myopic loss aversion and the equity premium puzzle. The Quarterly Journal of Economics, Vol. 110, Issue 1, 73–92. https://doi.org/10.2307/2118511
11. Gonzalez, R., Wu, G. (1999). On the shape of the probability weighting function. Cognitive Psychology, Vol. 38, Issue 1, 129–166. https://doi.org/10.1006/cogp.1998.0710
12. Kraus, A., Litzenberger, R.H. (1976). Skewness preference and the valuation of risk assets. The Journal of Finance, Vol. 31, No. 4, 1085–1100. https://doi.org/10.2307/2326275
13. Jiang, L., Wu, K., Zhou, G., Zhu, Y. (2020). Stock return asymmetry: Beyond skewness. Journal of Financial and Quantitative Analysis, Vol. 55, Issue 2, 357–386. https://doi.org/10.1017/S0022109019000206
14. Jiang, X., Han, L., Yin, L. (2018). Can skewness predict currency excess returns? The North American Journal of Economics and Finance, Vol. 48, 628–641. https://doi.org/10.1016/j.najef.2018.07.018
15. Han, Y., Mo, X., Su, Z., Zhu, Y. (2023). Is idiosyncratic asymmetry priced in commodity futures? Journal of Financial Research, Vol. 46, Issue 3, 875–898. https://doi.org/10.1111/jfir.12339
16. Karehnke, P. (2024). Systematic skewness and stock returns. The Review of Asset Pricing Studies, Vol. 14, Issue 4, 578–612. https://doi.org/10.1093/rapstu/raae010
17. Langlois H. (2020). Measuring skewness premia. Journal of Financial Economics, Vol. 135, Issue 2, 399–424. https://doi.org/10.1016/j.jfineco.2019.06.002
18. Fernandez-Perez, A., Frijns, B., Fuertes, A., Miffre, J. (2017). The skewness of commodity futures returns. Journal of Banking & Finance, Vol. 86, 143–158. https://doi.org/10.1016/j.jbankfin.2017.06.015
19. Wątorek, M., Drożdż S., Kwapień J., Minati L., Oświęcimka P., Stanuszek M. (2020). Multiscale characteristics of the emerging global cryptocurrency market. Physics Reports, Vol. 901, 1–82. https://doi.org/10.1016/j.physrep.2020.10.005
20. Friend, I., Westerfield, R. (1980). Co-skewness and capital asset pricing. The Journal of Finance, Vol. 35, Issue 4, 897–913. https://doi.org/10.1111/j.1540-6261.1980.tb03508.x
21. Jondeau, E., Zhang, Q., Zhu, X. (2019). Average skewness matters. Journal of Financial Economics, Vol. 134, Issue 1, 29–47. https://doi.org/10.1016/j.jfineco.2019.03.003
22. Harvey, C.R., Siddique, A. (2000). Conditional skewness in asset pricing tests. The Journal of Finance, Vol. 55, Issue 3, 1263–1295. https://doi.org/10.1111/0022-1082.00247
23. Liow, K.H., Chan, L.C.W.J. (2005). Co-skewness and co-kurtosis in global real estate securities. Journal of Property Research, Vol. 22, Issue 2–3, 163–203. https://doi.org/10.1080/09599910500453798
24. Liu, C.H., Hartzell, D.J., Grissom, T.V. (1992). The role of co-skewness in the pricing of real estate. The Journal of Real Estate Finance and Economics, Vol. 5, Issue 3, 299–319. https://doi.org/10.1007/bf02341917
25. Jia, Y., Liu, Y., Yan, S. (2020). Higher moments, extreme returns, and cross-section of cryptocurrency returns. Finance Research Letters, Vol. 39, 101536. https://doi.org/10.1016/j.frl.2020.101536
26. Ahmed, W.M., Mafrachi, M.A. (2020). Do higher-order realized moments matter for cryptocurrency returns? International Review of Economics & Finance, Vol. 72, 483–499. https://doi.org/10.1016/j.iref.2020.12.009
27. Zsolt, N.B., Botond, B. (2021). Co-skewness, co-kurtosis and their implications on asset pricing of cryptocurrencies. International Journal of Financial Markets and Derivatives, Vol. 8, No. 1, 65. https://doi.org/10.1504/ijfmd.2021.113860
28. Liu, Y., Tsyvinski, A., Wu, X. (2022). Common risk factors in cryptocurrency. The Journal of Finance, Vol. 77, Issue 2, 1133–1177. https://doi.org/10.1111/jofi.13119
29. Chen, Y., Liu, Y., Zhang, F. (2024). Coskewness and the short-term predictability for Bitcoin return. Technological Forecasting and Social Change, Vol. 200, 123196. https://doi.org/10.1016/j.techfore.2023.123196
30. Fama, E.F., French, K.R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, Vol. 33, Issue 1, 3–56. https://doi.org/10.1016/0304-405X(93)90023-5
31. Fama, E.F., French, K.R. (2015). A five-factor asset pricing model. Journal of Financial Economics, Vol. 116, Issue 1, 1–22. http://dx.doi.org/10.1016/j.jfineco.2014.10.010
32. Fang, H., Lai, T.-Y. (1997). Co-kurtosis and capital asset pricing. The Financial Review, Vol 32, Issue 2, 293–307. http://dx.doi.org/10.1111/j.1540-6288.1997.tb00426.x
33. Chan, K., Yang, J., Zhou, Y. (2013). What Makes Safe-Haven Currencies? Evidence from Conditional Co-Skewness. European Financial Management Association (EFMA), 51 p. Available at:: https://www.efmaefm.org/0efmameetings/efma%20annual%20meetings/2014-Rome/papers/EFMA2014_0225_fullpaper.pdf
34. Campbell, S., Song, Q., Wong, T.L. (2025). Macroscopic properties of equity markets: stylized facts and portfolio performance. Quantitative Finance, Vol. 25, Issue 9, 1375–1397. https://doi.org/10.1080/14697688.2025.2541859
35. Baltussen, G., Van Vliet, B., Van Vliet, P. (2021). The cross-section of stock returns before 1926 (and beyond). SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3969743
36. Rivin, I., Scevola, C. (2018). An investable сrypto-сurrency index. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3154706
37. Trimborn, S., Härdle, W.K. (2015). CRIX or evaluating blockchain based currencies. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2892594
38. Häusler, K., Xia, H. (2022). Indices on cryptocurrencies: an evaluation. Digital Finance, Vol. 4, Issue 2–3, 149–167. https://doi.org/10.1007/s42521-022-00048-8
39. Gil-Alana, L.A., Abakah, E.J.A., Rojo, M.F.R. (2019). Cryptocurrencies and stock market indices: Are they related? Research in International Business and Finance, Vol. 51, 101063. https://doi.org/10.1016/j.ribaf.2019.101063
40. Corbet, S., Lucey, B., Urquhart, A., Yarovaya, L. (2018). Cryptocurrencies as a financial asset: A systematic analysis. International Review of Financial Analysis, Vol. 62, 182–199. https://doi.org/10.1016/j.irfa.2018.09.003
Acknowledgements
The research was supported by the Russian Science Foundation under grant 25-28-00260, https://rscf.ru/project/25-28-00260/
About Authors
Anastasiia Alekseevna Dergileva
Research Assistant, Center for Financial Research and Data Analysis, National Research University Higher School of Economics, Moscow, Russia (109028, Moscow, Pokrovsky Boulevard, 11); ORCID https://orcid.org/0009-0008-6047-1775 е-mail: dergilevaana@gmail.com
Victoria Vladimirovna Dobrynskaya
Candidate of Economic Sciences, Associate Professor, School of Finance, National Research University Higher School of Economics, Moscow, Russia (109028, Moscow, Pokrovsky Boulevard, 11); ORCID https://orcid.org/0000-0001-7602-7240 e-mail: vdobrynskaya@hse.ru
Sergei Vycheslavovich Gurov
Candidate of Economic Sciences, Research Fellow, Center for Financial Research and Data Analysis, National Research University Higher School of Economics, Moscow, Russia (109028, Moscow, Pokrovsky Boulevard, 11); ORCID https://orcid.org/0000-0003-0104-5166 e-mail: sgurov@hse.ru
Tatiana Vladimirovna Sokolova
Candidate of Physics and Mathematics Sciences, Deputy Director, Center for Financial Research and Data Analysis, National Research University Higher School of Economics, Moscow, Russia (109028, Moscow, Pokrovsky Boulevard, 11); ORCID https://orcid.org/0000-0002-2238-7539 e-mail: tv.sokolova@hse.ru
For citation
Dergileva, A.A., Dobrynskaya, V.V., Gurov, S.V., Sokolova, T.V. (2026). Investment Behavior in the Global Cryptocurrency Market: Do Traders Take into Account the Possibilities of Diversification? Journal of Applied Economic Research, Vol. 25, No. 1, 249-282. https://doi.org/10.15826/vestnik.2026.25.1.009
Article info
Received October 13, 2025; Revised November 24, 2025; Accepted December 3, 2025.
DOI: http://dx.doi.org/10.15826/vestnik.2026.25.1.009
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