Volume 18, Issue 2 (6-2023)
J. Mon. Ec. 2023, 18(2): 263-284 |
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taleblou R, mohajeri P. Modeling the Dynamic Correlations among Cryptocurrencies: New Evidence from Multivariate Factor Stochastic Volatility Model. J. Mon. Ec. 2023; 18 (2) : 6
URL: http://jme.mbri.ac.ir/article-1-650-en.html
URL: http://jme.mbri.ac.ir/article-1-650-en.html
1- Associate Professor of Economics, Allameh Tabataba'i University
Abstract: (379 Views)
This paper intends to model the volatilities of returns of 20 different cryptocurrencies using daily data from 08/03/2018 to 09/20/2022. The multivariate factor stochastic volatility model (MFSV) within the framework of the nonlinear space-state approach is used. In this method, the cryptocurrency return volatility is decomposed into volatility rooted in latent factors and idiosyncratic volatility, and the time-varying pairwise correlation and dynamic covariance matrix are estimated in four sub-periods. The MFSV model’s results revealed that each sub-period contains a distinct number of latent factors, 2, 5, 4 and 2, which generally have a favorable impact on all cryptocurrency volatilities. The time-varying positive correlations between the return volatility of all cryptocurrencies are confirmed. Indeed, the strongest pairwise correlations belong to Ethereum, Litcoin, EOS, and VET in each sub-period, respectively. The DOGE, DOGE, Filecoin, and XRP, on the other hand, showedthe weakest correlations . As the pairwise correlations of cryptocurrency volatilities get strenger, especially during descending periods, it seems that the benefits of diversifying a crypto portfolio are getting less and less over time.
Article number: 6
Keywords: Factor Stochastic Volatility, Cryptocurrencies, Bayesian Approach, Heteroskedasticity, Dynamic Correlation
Type of Study: Original Research - Empirical |
Subject:
Economics
Received: 27 Nov 2023 | Accepted: 16 Jan 2024 | Published: 30 Jul 2024
Received: 27 Nov 2023 | Accepted: 16 Jan 2024 | Published: 30 Jul 2024
References
1. Agosto, A., & Cafferata, A. (2020). Financial bubbles: a study of co-explosivity in the cryptocurrency market. Risks, 8(2), 34. [DOI:10.3390/risks8020034]
2. Al-Yahyaee, K. H., Rehman, M. U., Mensi, W., & Al-Jarrah, I. M. W. (2019). Can uncertainty indices predict Bitcoin prices? A revisited analysis using partial and multivariate wavelet approaches. The North American Journal of Economics and Finance, 49, 47-56. [DOI:10.1016/j.najef.2019.03.019]
3. Antonakakis, N., Chatziantoniou, I., & Gabauer, D. (2019). Cryptocurrency market contagion: Market uncertainty, market complexity, and dynamic portfolios. Journal of International Financial Markets, Institutions and Money, 61, 37-51. [DOI:10.1016/j.intfin.2019.02.003]
4. Baumöhl, E. (2019). Are cryptocurrencies connected to forex? A quantile cross-spectral approach. Finance Research Letters, 29, 363-372. [DOI:10.1016/j.frl.2018.09.002]
5. Baur, D. G., & Dimpfl, T. (2018). Asymmetric volatility in cryptocurrencies. Economics Letters, 173, 148-151. [DOI:10.1016/j.econlet.2018.10.008]
6. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327. [DOI:10.1016/0304-4076(86)90063-1]
7. Borri, N. (2019). Conditional tail-risk in cryptocurrency markets. Journal of Empirical Finance, 50, 1-19. [DOI:10.1016/j.jempfin.2018.11.002]
8. Bouri, E., Azzi, G., & Dyhrberg, A. H. (2017). On the return-volatility relationship in the Bitcoin market around the price crash of 2013. Economics, 11(1). [DOI:10.5018/economics-ejournal.ja.2017-2]
9. Bouri, E., Molnár, P., Azzi, G., Roubaud, D., & Hagfors, L. I. (2017). On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier? Finance Research Letters, 20, 192-198. [DOI:10.1016/j.frl.2016.09.025]
10. Cagli, E. C. (2019). Explosive behavior in the prices of Bitcoin and altcoins. Finance Research Letters, 29, 398-403. [DOI:10.1016/j.frl.2018.09.007]
11. Caporale, G. M., & Zekokh, T. (2019). Modeling volatility of cryptocurrencies using Markov-Switching GARCH models. Research in International Business and Finance, 48, 143-155. [DOI:10.1016/j.ribaf.2018.12.009]
12. Cebrián-Hernández, Á., & Jiménez-Rodríguez, E. (2021). Modeling of the Bitcoin Volatility through Key Financial Environment Variables: An Application of Conditional Correlation MGARCH Models. Mathematics, 9(3), 267. [DOI:10.3390/math9030267]
13. Chan, W. H., Le, M., & Wu, Y. W. (2019). Holding Bitcoin longer: The dynamic hedging abilities of Bitcoin. The Quarterly Review of Economics and Finance, 71, 107-113. [DOI:10.1016/j.qref.2018.07.004]
14. Charfeddine, L., & Maouchi, Y. (2019). Are shocks on the returns and volatility of cryptocurrencies really persistent? Finance Research Letters, 28, 423-430. [DOI:10.1016/j.frl.2018.06.017]
15. Charles, A., & Darné, O. (2019). Volatility estimation for Bitcoin: Replication and robustness. International Economics, 157, 23-32. [DOI:10.1016/j.inteco.2018.06.004]
16. Cheah, E.-T., & Fry, J. (2015). Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin. Economics Letters, 130, 32-36. [DOI:10.1016/j.econlet.2015.02.029]
17. Choi, S. H., & Jarrow, R. A. (2020). Testing the local martingale theory of bubbles using cryptocurrencies. Available at SSRN. [DOI:10.2139/ssrn.3701960]
18. Chu, J., Chan, S., Nadarajah, S., & Osterrieder, J. (2017). GARCH modelling of cryptocurrencies. Journal of Risk and Financial Management, 10(4), 17. [DOI:10.3390/jrfm10040017]
19. Chuen, D. L. E. E. K., Guo, L., & Wang, Y. (2017). Cryptocurrency: A new investment opportunity? The Journal of Alternative Investments, 20(3), 16-40. [DOI:10.3905/jai.2018.20.3.016]
20. Coinmarket cap. (2021). http://coinmarketcap.com
21. Conrad, C., Custovic, A., & Ghysels, E. (2018). Long-and short-term cryptocurrency volatility components: A GARCH-MIDAS analysis. Journal of Risk and Financial Management, 11(2), 23. [DOI:10.3390/jrfm11020023]
22. 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. [DOI:10.1016/j.econlet.2018.01.004]
23. Dyhrberg, A. H. (2016a). Bitcoin, gold and the dollar-A GARCH volatility analysis. Finance Research Letters, 16, 85-92. [DOI:10.1016/j.frl.2015.10.008]
24. Dyhrberg, A. H. (2016b). Hedging capabilities of bitcoin. Is it the virtual gold? Finance Research Letters, 16, 139-144. [DOI:10.1016/j.frl.2015.10.025]
25. Fakhfekh, M., & Jeribi, A. (2020). Volatility dynamics of cryptocurrencies returns: Evidence from asymmetric and long memory GARCH models. Research in International Business and Finance, 51, 101075. [DOI:10.1016/j.ribaf.2019.101075]
26. Fendi, U. A., Tahtamouni, A., Jalghoum, Y., & Suleiman, J. M. (2019). The study of bubbles in bitcoin behavior. Banks and Bank Systems, 14(4), 133. [DOI:10.21511/bbs.14(4).2019.13]
27. Guizani, S., & Nafti, I. K. (2019). The Determinants of Bitcoin Price Volatility: An Investigation With ARDL Model. Procedia Computer Science, 164, 233-238. [DOI:10.1016/j.procs.2019.12.177]
28. Hafner, C. M. (2020). Testing for bubbles in cryptocurrencies with time-varying volatility. Journal of Financial Econometrics, 18(2), 233-249.
29. Harjunpää, R. A. (2017). CRYPTOCURRENCY CORRELATION ANALYSIS. Bachelor's thesis, Programme: Business Administration, specialization ….
30. Huynh, T. L. D., Nguyen, S. P., & Duong, D. (2018). Contagion risk measured by return among cryptocurrencies. International Econometric Conference of Vietnam, 987-998. [DOI:10.1007/978-3-319-73150-6_71]
31. Jaroenwiriyakul, S., & Tanomchat, W. (2020). Exploring the Dynamic Relationships between Cryptocurrencies and Stock Markets in the ASEAN-5. วารสาร เศรษฐศาสตร์ และ กลยุทธ์ การ จัดการ (Journal of Economics and Management Strategy), 7(1), 129-144.
32. Ji, Q., Bouri, E., Gupta, R., & Roubaud, D. (2018). Network causality structures among Bitcoin and other financial assets: A directed acyclic graph approach. The Quarterly Review of Economics and Finance, 70, 203-213. [DOI:10.1016/j.qref.2018.05.016]
33. 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. [DOI:10.1016/j.irfa.2018.12.002]
34. Kastner, G., Frühwirth-Schnatter, S., & Lopes, H. F. (2017). Efficient Bayesian inference for multivariate factor stochastic volatility models. Journal of Computational and Graphical Statistics, 26(4), 905-917. [DOI:10.1080/10618600.2017.1322091]
35. Katsiampa, P. (2017). Volatility estimation for Bitcoin: A comparison of GARCH models. Economics Letters, 158, 3-6. [DOI:10.1016/j.econlet.2017.06.023]
36. Katsiampa, P. (2019). Volatility co-movement between Bitcoin and Ether. Finance Research Letters, 30, 221-227. [DOI:10.1016/j.frl.2018.10.005]
37. Kim, J.-M., Kim, S.-T., & Kim, S. (2020). On the relationship of cryptocurrency price with us stock and gold price using copula models. Mathematics, 8(11), 1859. [DOI:10.3390/math8111859]
38. Klein, T., Thu, H. P., & Walther, T. (2018). Bitcoin is not the New Gold-A comparison of volatility, correlation, and portfolio performance. International Review of Financial Analysis, 59, 105-116. [DOI:10.1016/j.irfa.2018.07.010]
39. Kurka, J. (2019). Do cryptocurrencies and traditional asset classes influence each other? Finance Research Letters, 31, 38-46. [DOI:10.1016/j.frl.2019.04.018]
40. Kyriazis, N. A. (2019). A survey on efficiency and profitable trading opportunities in cryptocurrency markets. Journal of Risk and Financial Management, 12(2), 67. [DOI:10.3390/jrfm12020067]
41. Lahajnar, S., & Rožanec, A. (2020). The correlation strength of the most important cryptocurrencies in the bull and bear market. International Management and Financial Innovation, 17(3), 67-81. [DOI:10.21511/imfi.17(3).2020.06] [PMID]
42. Le Tran, V., & Leirvik, T. (2020). Efficiency in the markets of crypto-currencies. Finance Research Letters, 35, 101382. [DOI:10.1016/j.frl.2019.101382]
43. Luu Duc Huynh, T. (2019). Spillover risks on cryptocurrency markets: A look from VAR-SVAR granger causality and student'st copulas. Journal of Risk and Financial Management, 12(2), 52. [DOI:10.3390/jrfm12020052]
44. Nadarajah, S., & Chu, J. (2017). On the inefficiency of Bitcoin. Economics Letters, 150, 6-9. [DOI:10.1016/j.econlet.2016.10.033]
45. Naimy, V. Y., & Hayek, M. R. (2018). Modeling and predicting the Bitcoin volatility using GARCH models. International Journal of Mathematical Modelling and Numerical Optimisation, 8(3), 197-215.
https://doi.org/10.1504/IJMMNO.2018.088994 [DOI:10.1504/IJMMNO.2018.10009955]
46. Nekhili, R., & Sultan, J. (2020). Jump Driven Risk Model Performance in Cryptocurrency Market. International Journal of Financial Studies, 8(2), 19. [DOI:10.3390/ijfs8020019]
47. Omane-Adjepong, M., & Alagidede, I. P. (2019). Multiresolution analysis and spillovers of major cryptocurrency markets. Research in International Business and Finance, 49, 191-206. [DOI:10.1016/j.ribaf.2019.03.003]
48. Peng, Y., Albuquerque, P. H. M., de Sá, J. M. C., Padula, A. J. A., & Montenegro, M. R. (2018). The best of two worlds: Forecasting high-frequency volatility for cryptocurrencies and traditional currencies with Support Vector Regression. Expert Systems with Applications, 97, 177-192. [DOI:10.1016/j.eswa.2017.12.004]
49. Phillip, A., Chan, J. S. K., & Peiris, S. (2018). A new look at Cryptocurrencies. Economics Letters, 163, 6-9. [DOI:10.1016/j.econlet.2017.11.020]
50. Rehman, M. U., & Apergis, N. (2019). Determining the predictive power between cryptocurrencies and real-time commodity futures: Evidence from quantile causality tests. Resources Policy, 61, 603-616. [DOI:10.1016/j.resourpol.2018.08.015]
51. Samah, H. (2020). Bitcoin Hedging and Diversification Capabilities: An International Evidence. Global Journal of Management And Business Research.
52. Scopus database. (2021). https://www.scopus.com/home.uri
53. Sensoy, A. (2019). The inefficiency of Bitcoin revisited: A high-frequency analysis with alternative currencies. Finance Research Letters, 28, 68-73. [DOI:10.1016/j.frl.2018.04.002]
54. Shi, Y., Tiwari, A. K., Gozgor, G., & Lu, Z. (2020). Correlations among cryptocurrencies: Evidence from multivariate factor stochastic volatility model. Research in International Business and Finance, 53, 101231. [DOI:10.1016/j.ribaf.2020.101231]
55. Stavroyiannis, S., & Babalos, V. (2017). Dynamic properties of the Bitcoin and the US market. Available at SSRN 2966998. [DOI:10.2139/ssrn.2966998]
56. Stensås, A., Nygaard, M. F., Kyaw, K., & Treepongkaruna, S. (2019). Can Bitcoin be a diversifier, hedge or safe haven tool? Cogent Economics & Finance, 7(1), 1593072. [DOI:10.1080/23322039.2019.1593072]
57. Taylor, J. B. (1986). New econometric approaches to stabilization policy in stochastic models of macroeconomic fluctuations. Handbook of Econometrics, 3, 1997-2055. [DOI:10.1016/S1573-4412(86)03014-3]
58. Urquhart, A. (2016). The inefficiency of Bitcoin. Economics Letters, 148, 80-82. [DOI:10.1016/j.econlet.2016.09.019]
59. Yamauchi, Y., & Omori, Y. (2020). Multivariate stochastic volatility model with realized volatilities and pairwise realized correlations. Journal of Business & Economic Statistics, 38(4), 839-855. [DOI:10.1080/07350015.2019.1602048]
60. Zahid, M, & Iqbal, F. (2020). Modeling the Volatility of Cryptocurrencies: An Empirical Application of Stochastic Volatility Models. Sains Malaysiana, 49(3), 703-712. [DOI:10.17576/jsm-2020-4903-25]
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