DSpace Repository
The Analysis of Multifractal Cross-Correlation Connectedness Between Bitcoin and the Stock Market
JavaScript is disabled for your browser. Some features of this site may not work without it.
| dc.contributor.author | Bielinskyi, Andrii | |
| dc.contributor.author | Soloviev, Vladimir | |
| dc.contributor.author | Solovieva, Victoria | |
| dc.contributor.author | Matviychuk, Andriy | |
| dc.contributor.author | Семеріков, Сергій Олексійович | |
| dc.contributor.author | Соловйов, Володимир Миколайович | |
| dc.date.accessioned | 2023-07-01T13:25:38Z | |
| dc.date.available | 2023-07-01T13:25:38Z | |
| dc.date.issued | 2023-06-18 | |
| dc.identifier.citation | Bielinskyi A. The Analysis of Multifractal Cross-Correlation Connectedness Between Bitcoin and the Stock Market / Andrii Bielinskyi, Vladimir Soloviev, Victoria Solovieva, Andriy Matviychuk, Serhiy Semerikov // Information Technology for Education, Science and Technics: Proceedings of ITEST 2022 / Editors : Emil Faure, Olena Danchenko, Maksym Bondarenko, Yurii Tryus, Constantine Bazilo, Grygoriy Zaspa // Lecture Notes on Data Engineering and Communications Technologies. – Cham : Springer, 2023. – Vol. 178. – P. 323–345. – DOI : https://doi.org/10.1007/978-3-031-35467-0_21 | uk |
| dc.identifier.isbn | 978-3-031-35466-3 | |
| dc.identifier.isbn | 978-3-031-35467-0 | |
| dc.identifier.uri | https://doi.org/10.1007/978-3-031-35467-0_21 | |
| dc.identifier.uri | http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/7409 | |
| dc.description | 1. Aysan, A.F., Demir, E., Gozgor, G., Lau, C.K.M.: Effects of the geopolitical risks on Bitcoin returns and volatility. Res. Int. Bus. Financ. 47, 511–518 (2019) 2. Bariviera, A.F., Merediz-Sola, I.: Where do we stand in cryptocurrencies economic research? A survey based on hybrid analysis. J. Econ. Surv. 35, 377–407 (2021) 3. Bielinskyi, A., Semerikov, S., Serdyuk, O., Solovieva, V., Soloviev, V., Pichl, L.: Econophysics of sustainability indices. In: CEUR Workshop Proceedings, vol. 2713, pp. 372–392 (2020) 4. Bielinskyi, A., Soloviev, V.: Complex network precursors of crashes and critical events in the cryptocurrency market. In: CEUR Workshop Proceedings, vol. 2292, pp. 37–45 (2018) 5. Bielinskyi, A.O., Hushko, S.V., Matviychuk, A.V., Serdyuk, O.A., Semerikov, S.O., Soloviev, V.N.: Irreversibility of financial time series: a case of crisis. In: CEUR Workshop Proceedings, vol. 3048, pp. 134–150 (2021) 6. Bielinskyi, A.O., Serdyuk, O.A., Semerikov, S.O., Soloviev, V.N.: Econophysics of cryptocurrency crashes: a systematic review. In: CEUR Workshop Proceedings, vol. 3048, pp. 31–133 (2021) 7. Buszko, M., Orzeszko, W., Stawarz, M.: COVID-19 pandemic and stability of stock market - a sectoral approach. PLoS ONE 16, e0250938 (2021) 8. Chahuán-Jiménez, K., Rubilar, R., de la Fuente-Mella, H., Leiva, V.: Breakpoint analysis for the COVID-19 pandemic and its effect on the stock markets. Entropy 23, 100 (2021) 9. Chen, S.-P., He, L.-Y.: Multifractal spectrum analysis of nonlinear dynamical mechanisms in China’s agricultural futures markets. Phys. A 389, 1434–1444 (2010) 10. Corbet, S., Lucey, B., Urquhart, A., Yarovaya, L.: Cryptocurrencies as a financial asset: a systematic analysis. Int. Rev. Financ. Anal. 62, 182–199 (2019) 11. Dai, M., Hou, J., Ye, D.: Multifractal detrended fluctuation analysis based on fractal fitting: the long-range correlation detection method for highway volume data. Phys. A 444, 722–731 (2016) 12. Dai, M., Zhang, C., Zhang, D.: Multifractal and singularity analysis of highway volume data. Phys. A 407, 332–340 (2014) 13. Dewandaru, G., Masih, R., Bacha, O., Masih, A.M.M.: Developing trading strategies based on fractal finance: an application of MF-DFA in the context of Islamic equities. Phys. A 438, 223–235 (2015) 14. Drożdż, S., Kowalski, R., Oświȩcimka, P., Rak, R., Gȩbarowski, R.: Dynamical variety of shapes in financial multifractality. Complexity 2018, 13 (2018) 15. Drożdż, S., Kwapień, J., Oświ ̨ecimka, P., Stanisz, T., W ̨atorek, M.: Complexity in economic and social systems: cryptocurrency market at around COVID-19. Entropy 22, 1043 (2020) 16. Drożdż, S., Oświȩcimka, P.: Detecting and interpreting distortions in hierarchical organization of complex time series. Phys. Rev. E. 91, 030902 (2015) 17. Flori, A.: Cryptocurrencies in finance: review and applications. Int. J. Theor. Appl. Financ. 22, 1950020 (2019) 18. Frisch, U., Parisi, G.: On the singularity structure of fully developed turbulence. In: Ghil, M., Benzi, R., Parisi, G. (eds.) Turbulence and Predictability of Geophysical Flows and Climate Dynamics, pp. 84–88. North-Holland, New York (1985) 19. Gerlach, J.-C., Demos, G., Sornette, D.: Dissection of Bitcoin’s multiscale bubble history from January 2012 to February 2018. R. Soc. Open Sci. 6, 180643 (2019) 20. Grassberger, P.: Generalized dimensions of strange attractors. Phys. Lett. A 97, 227–230 (1983) 21. Halsey, T.C., Jensen, M.H., Kadanoff, L.P., Procaccia, I., Shraiman, B.I.: Fractal measures and their singularities: the characterization of strange sets. Phys. Rev. A 33, 1141 (1986) 22. Hurst, H.E.: Long-term storage capacity of reservoirs. Trans. Am. Soc. Civ. Eng. 116, 770–799 (1951) 23. Ihlen, E.A.F.: Introduction to multifractal detrended fluctuation analysis in Matlab. Front. Physiol. 3, 141 (2012) 24. James, N., Menzies, M.: Association between COVID-19 cases and international equity indices. Phys. D 417, 132809 (2021) 25. James, N., Menzies, M.: Efficiency of communities and financial markets during the 2020 pandemic. Chaos 31, 083116 (2021) 26. Jiang, Z.-Q., Zhou, W.-X.: Multifractal detrending moving-average cross-correlation analysis. Phys. Rev. E 84, 016106 (2011)22 27. Kantelhardt, J.W., Zschiegner, S.A., Koscienlny-Bunde, E., Bunde, A., Havlin, S., Stanley, H.E.: Multifractal detrended fluctuation analysis of non-stationary time series. Phys. A 316, 87–114 (2002) 28. Katsiampa, P., Yarovaya, L., Zi ̨eba, D.: High-frequency connectedness between Bitcoin and other top-traded crypto assets during the COVID-19 crisis. J. Int. Fin. Mark. Inst. Money (2022). https://doi.org/10.1016/j.intfin.2022.101578 29. Kiv, A.E., et al.: Machine learning for prediction of emergent economy dynamics. In: CEUR Workshop Proceedings, vol. 3048, pp. i–xxxi (2021) 30. Kristoufek, L.: Multifractal height cross-correlation analysis: a new method for analyzing long-range cross-correlations. EPL (Europhys. Lett.) 95, 68001 (2011) 31. Li, J., Lu, X., Zhou, Y.: Cross-correlations between crude oil and exchange markets for selected oil rich economies. Phys. A 453, 131–143 (2016) 32. Lo, A.W.: Long-term memory in stock market prices. Econometrica 59, 1279–1313 (1991) 33. Lu, X., Li, J., Zhou, Y., Qian, Y.: Cross-correlations between RMB exchange rate and international commodity markets. Phys. A 486, 168–182 (2017) 34. Lu, X., Tian, J., Zho, Y., Li, Z.: Multifractal detrended fluctuation analysis of the Chinese stock index futures market. Phys. A 392, 1452–1458 (2013) 35. Ma, F., Wei, Y., Huang, D., Zhao, L.: Cross-correlations between West Texas intermediate crude oil and the stock markets of the BRIC. Phys. A 392, 5356–5368 (2013) 36. Ma, F., Wei, Y., Huang, D.: Multifractal detrended cross-correlation analysis between the Chinese stock market and surrounding stock markets. Phys. A 392, 1659–1670 (2013) 37. Maheu, J.M., McCurdy, T.H., Song, Y.: Bull and bear markets during the COVID-19 pandemic. Fin. Res. Lett. 42, 102091 (2021) 38. Meakin, P.: Fractals, Scaling and Growth far from Equilibrium. Cambridge University Press, Cambridge (1998) 39. Oświȩcimka, P., Livi, L., Drożdż, S.: Right-side-stretched multifractal spectra indicate small-worldness in networks. Commun. Nonlinear Sci. Numer. Simul. 57, 231–245 (2018) 40. Peng, C.K., Buldyrev, S.V., Havlin, S., Simons, M., Stanley, H.E., Goldberger, A.L.: Mosaic organization of DNA nucleotides. Phys. Rev. E 49, 1685–1689 (1994) 41. Podobnik, B., Stanley, H.E.: Detrended cross-correlation analysis: a new method for analyzing two non-stationary time series. Phys. Rev. Lett. 100, 084102 (2008) 42. Qian, X.-Y., Liu, Y.-M., Jiang, Z.-Q., Podobnik, B., Zhou, W.-X., Stanley, H.E.: Detrended partial cross-correlation analysis of two nonstationary time series influenced by common external forces. Phys. Rev. E 91, 062816 (2015) 43. Soloviev, V., Bielinskyi, A., Serdyuk, O., Solovieva, V., Semerikov, S.: Lyapunov exponents as indicators of the stock market crashes. In: CEUR Workshop Proceedings, vol. 2732, pp. 455–470 (2020) 44. Soloviev, V., Bielinskyi, A., Solovieva, V.: Entropy analysis of crisis phenomena for DJIA index. In: CEUR Workshop Proceedings, vol. 2393, pp. 434–449 (2019) 45. Soloviev, V.N., Bielinskyi, A.O., Kharadzjan, N.A.: Coverage of the coronavirus pandemic through entropy measures. In: CEUR Workshop Proceedings, vol. 2832, pp. 24–42 (2020) 46. Song, R., Shu, M., Zhu, W.: The 2020 global stock market crash: endogenous or exogenous? Phys. A. 585, 126425 (2022) 47. Sornette, D.: Critical Phenomena in Natural Sciences: Chaos, Fractals, Self-Organization and Disorder. Concepts and Tools. Springer, Heidelberg (2006). https://doi.org/10.1007/3-540-33182-4 48. The official page of “Yahoo! Finance” (1997). https://finance.yahoo.com 49. Wang, J., Shang, P., Ge, W.: Multifractal cross-correlation analysis based on statistical moments. Fractals 20, 271–279 (2012) 50. W ̨atorek, M., Drożdż, S., Kwapień, J., Minati, L., Oświ ̨ecimka, P., Stanuszek, M.: Multiscale characteristics of the emerging global cryptocurrency market. Phys. Rep. 901, 1–82 (2021) 51. Xia, S., Huiping, C., Ziqin, W., Yongzhuang, Y.: Multifractal analysis of Hang Seng index in Hong Kong stock market. Phys. A 291, 553–562 (2001) 52. Zebende, G.: DCCA cross-correlation coefficient: Quantifying level of cross-correlation. Phys. A 390, 614–618 (2011) 53. Zhang, D., Hu, M., Ji, Q.: Financial markets under the global pandemic of COVID-19. Fin. Res. Lett. 36, 101528 (2020) 54. Zhang, W., Wang, P., Li, X., Shen, D.: Twitter’s daily happiness sentiment and international stock returns: evidence from linear and nonlinear causality tests. J. Behave. Exp. Fin. 18, 50–53 (2018) 55. Zhang, Z., Zhang, Y., Shen, D., Zhang, W.: The dynamic cross-correlations between mass media news, new media news, and stock returns. Complexity 2018, 1–11 (2018) 56. Zhou, W.X.: Multifractal detrended cross-correlation analysis for two nonstationary signals. Phys. Rev. E 77, 066211 (2008) 57. Zou, Y., Donner, R.V., Marwan, N., Donges, J.F., Kurths, J.: Complex network approaches to nonlinear time series analysis. Phys. Rep. 787, 1–97 (2019) | uk |
| dc.description.abstract | In this study, we examine the multifractal cross-correlation relationships between stock and cryptocurrency markets. The measures of complexity which can serve as indicators (indicators-precursors) in both markets are retrieved from Multifractal Detrended Cross-Correlation Analysis. On the example of the S&P 500 and HSI stock indices that are used most by investors to gauge the status of the economy in the world, and the cryptocurrency Bitcoin, which mostly determines the existence of the crypto market, we assess the variation of multifractality and correlations in both markets. Using the sliding window approach, we localize their dynamics across time and indicate a high degree of non-linearity with dominant anti-persistency during crash periods for each index. The existence of periods with high and low cross-correlations for stock and crypto markets provides prospects for reliable trading with several pairs of assets and effective diversification of their risks. | uk |
| dc.language.iso | en | uk |
| dc.publisher | Springer, Cham | uk |
| dc.subject | Stock market | uk |
| dc.subject | crypto market | uk |
| dc.subject | cross-correlations | uk |
| dc.subject | multifractal analysis | uk |
| dc.subject | crash | uk |
| dc.subject | complex systems | uk |
| dc.subject | indicator-precursor | uk |
| dc.title | The Analysis of Multifractal Cross-Correlation Connectedness Between Bitcoin and the Stock Market | uk |
| dc.type | Article | uk |
