Research Article
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Stability Analysis of Bitcoin using Recurrence Quantification Analysis

Year 2022, Volume 4, Issue 2, 104 - 110, 30.07.2022
https://doi.org/10.51537/chaos.1112188

Abstract

Cryptocurrencies are new kinds of electronic currencies based on communication technologies. These currencies have attracted the attention of investors. However, cryptocurrencies are very volatile and unpredictable. For investors, it is very difficult to make investment decisions in cryptocurrency market. Therefore, revealing changes in the dynamics of cryptocurrencies are valuable for investors. Bitcoin is the most popular and representative cryptocurrency in cryptocurrency market. In this study how dynamical properties of Bitcoin changed through time is analyzed with recurrence quantification analysis (RQA). RQA is a pattern recognition-based time series analysis method that reveals dynamics of the time series by calculating some metrics called RQA measures. This method has been successfully applied to nonlinear, nonstationary, short and chaotic time series and does not assume a statistical model. RQA can reveal important properties of time series data such as determinism, laminarity, stability, randomness, regularity and complexity. By using sliding window RQA we show that in 2021 RQA measures for Bitcoin prices collapse and Bitcoin becomes more unpredictable, more random, more unstable, more irregular and less complex. Therefore, dynamics and stability of the Bitcoin prices significantly changed in 2021.

References

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  • Aste, T. (2019). Cryptocurrency market structure: connecting emotions and economics. Digital Finance, 1(1), 5-21
  • Bastos, J. A., & Caiado, J. (2011). Recurrence quantification analysis of global stock markets. Physica A: Statistical Mechanics and its Applications, 390(7), 1315-1325.
  • Chaim, P., & Laurini, M. P. (2019). Nonlinear dependence in cryptocurrency markets. The North American Journal of Economics and Finance, 48, 32-47.
  • Eckmann, J. P., Kamphorst, S. O., & Ruelle, D. (1987). Recurrence Plots of Dynamical Systems. Europhysics Letters (EPL), 4(9), 973-977. doi:10.1209/0295-5075/4/9/004
  • Härdle, W. K., Harvey, C. R., & Reule, R. C. (2020). Understanding cryptocurrencies. Journal of Financial Econometrics, 18(2), 181-208.
  • Huffaker, R. G., Huffaker, R., Bittelli, M., & Rosa, R. (2017). Nonlinear time series analysis with R. Oxford University Press.
  • Marwan, N., Wessel, N., Meyerfeldt, U., Schirdewan, A., & Kurths, J. (2002). Recurrence-plot-based measures of complexity and their application to heart-rate-variability data. Physical review E, 66(2), 026702.
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  • Moloney, K., & Raghavendra, S. (2012). Examining the dynamical transition in the Dow Jones Industrial. Physics Letters A, 223(4), 255-260.
  • Packard, N. H., Crutchfield, J. P., Farmer, J. D., & Shaw, R. S. (1980). Geometry from a Time Series. Physical Review Letters, 45(9), 712-716. doi:10.1103/physrevlett.45.712
  • Piskun, O., & Piskun, S. (2011). Recurrence quantification analysis of financial market crashes and crises. arXiv preprint arXiv:1107.5420.
  • Sasikumar, A., & Kamaiah, B. (2014). A complex dynamical analysis of the Indian stock market. Economics Research International, vol. 2014, Article ID 807580. doi.org/10.1155/2014/807580
  • Soloviev, V. N., & Belinskiy, A. (2018). Complex systems theory and crashes of cryptocurrency market. In V. Ermolayev, M. C. Suárez-Figueroa, V. Yakovyna, H. C. Mayr, M. Nikitchenko, A. Spivakovsky (Eds.), International Conference on Information and Communication Technologies in Education, Research, and Industrial Applications (pp. 276-297). Springer. doi.org/10.1007/978-3-030-13929-2_14
  • Soloviev, V., Serdiuk, O., Semerikov, S., & Kiv, A. (2020). Recurrence plot-based analysis of financial-economic crashes. In: Kiv, A. (Ed.) Machine Learning for Prediction of Emergent Economy Dynamics, Proceedings of the Selected Papers of the Special Edition of International Conference on Monitoring, Modeling & Management of Emergent Economy (pp. 21-40). CEUR Workshop Proceedings.
  • Strozzi, F., Zaldívar, J.-M., & Zbilut, J. P. (2007). Recurrence Quantification Analysis and State Space Divergence Reconstruction for Financial Time Series Analysis. Physica A: Statistical Mechanics and its Applications, 376, 487-499. doi:10.1016/j.physa.2006.10.020
  • Strozzi, F., Gutiérrez, E., Noè, C., Rossi, T., Serati, M., & Zaldívar, J. M. (2008). Measuring Volatility in the Nordic Spot Electricity Market Using Recurrence Quantification Analysis. European Physical Journal Special Topics, 164(1), 105-115. doi:10.1140/epjst/e2008-00837-1
  • Takens, F. (1981). Detecting Strange Attractors in Turbulence. In R. D. & Y. L.S. (Eds.), Dynamical Systems and Turbulence (pp. 366-381). Berlin, Heidelberg: Springer.
  • Tredinnick, L. (2019). Cryptocurrencies and the blockchain. Business Information Review, 36(1), 39-44.
  • Webber, C. L., Jr., & Zbilut, J. P. (1994). Dynamical Assessment of Physiological Systems and States Using Recurrence Plot Strategies. Journal of Applied Physiology, 76(2), 965-973. doi:10.1152/jappl.1994.76.2.965
  • Yuan, Y., & Wang, F. Y. (2018). Blockchain and cryptocurrencies: Model, techniques, and applications. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(9), 1421-1428.
  • Xing, Y., & Wang, J. (2020). Linkages between global crude oil market volatility and financial market by complexity synchronization. Empirical Economics, 59(5), 2405-2421. doi:10.1007/s00181-019-01762-w
  • Zbilut, J. P. (2005). Use of Recurrence Quantification Analysis in Economic Time Series. In M. Salzano & A. Kirman (Eds.), Economics: Complex Windows (pp. 91-104). Milano: Springer.
  • Zbilut, J. P., & Webber, C. L. (1992). Embeddings and Delays as Derived from Quantification of Recurrence Plots. Physics Letters A, 171(3-4), 199-203. doi:10.1016/0375-9601(92)90426-m

Year 2022, Volume 4, Issue 2, 104 - 110, 30.07.2022
https://doi.org/10.51537/chaos.1112188

Abstract

References

  • Alqaralleh, H., Abuhommous, A. A., & Alsaraireh, A. (2020). Modelling and forecasting the volatility of cryptocurrencies: A comparison of nonlinear GARCH-Type Models. International Journal of Financial Research, 11(4), 346-356.
  • Aste, T. (2019). Cryptocurrency market structure: connecting emotions and economics. Digital Finance, 1(1), 5-21
  • Bastos, J. A., & Caiado, J. (2011). Recurrence quantification analysis of global stock markets. Physica A: Statistical Mechanics and its Applications, 390(7), 1315-1325.
  • Chaim, P., & Laurini, M. P. (2019). Nonlinear dependence in cryptocurrency markets. The North American Journal of Economics and Finance, 48, 32-47.
  • Eckmann, J. P., Kamphorst, S. O., & Ruelle, D. (1987). Recurrence Plots of Dynamical Systems. Europhysics Letters (EPL), 4(9), 973-977. doi:10.1209/0295-5075/4/9/004
  • Härdle, W. K., Harvey, C. R., & Reule, R. C. (2020). Understanding cryptocurrencies. Journal of Financial Econometrics, 18(2), 181-208.
  • Huffaker, R. G., Huffaker, R., Bittelli, M., & Rosa, R. (2017). Nonlinear time series analysis with R. Oxford University Press.
  • Marwan, N., Wessel, N., Meyerfeldt, U., Schirdewan, A., & Kurths, J. (2002). Recurrence-plot-based measures of complexity and their application to heart-rate-variability data. Physical review E, 66(2), 026702.
  • Marwan, N., Romano, M. C., Thiel, M., & Kurths, J. (n.d.). A Comprehensive Bibliography About RPs, RQA And Their Applications. Retrieved December 24, 2021, from http://www.recurrence-plot.tk/bibliography.php
  • Mezquita, Y., Gil-González, A. B., Prieto, J., & Corchado, J. M. (2021). Cryptocurrencies and Price Prediction: A Survey. In International Congress on Blockchain and Applications (pp. 339-346). Springer, Cham.
  • Moloney, K., & Raghavendra, S. (2012). Examining the dynamical transition in the Dow Jones Industrial. Physics Letters A, 223(4), 255-260.
  • Packard, N. H., Crutchfield, J. P., Farmer, J. D., & Shaw, R. S. (1980). Geometry from a Time Series. Physical Review Letters, 45(9), 712-716. doi:10.1103/physrevlett.45.712
  • Piskun, O., & Piskun, S. (2011). Recurrence quantification analysis of financial market crashes and crises. arXiv preprint arXiv:1107.5420.
  • Sasikumar, A., & Kamaiah, B. (2014). A complex dynamical analysis of the Indian stock market. Economics Research International, vol. 2014, Article ID 807580. doi.org/10.1155/2014/807580
  • Soloviev, V. N., & Belinskiy, A. (2018). Complex systems theory and crashes of cryptocurrency market. In V. Ermolayev, M. C. Suárez-Figueroa, V. Yakovyna, H. C. Mayr, M. Nikitchenko, A. Spivakovsky (Eds.), International Conference on Information and Communication Technologies in Education, Research, and Industrial Applications (pp. 276-297). Springer. doi.org/10.1007/978-3-030-13929-2_14
  • Soloviev, V., Serdiuk, O., Semerikov, S., & Kiv, A. (2020). Recurrence plot-based analysis of financial-economic crashes. In: Kiv, A. (Ed.) Machine Learning for Prediction of Emergent Economy Dynamics, Proceedings of the Selected Papers of the Special Edition of International Conference on Monitoring, Modeling & Management of Emergent Economy (pp. 21-40). CEUR Workshop Proceedings.
  • Strozzi, F., Zaldívar, J.-M., & Zbilut, J. P. (2007). Recurrence Quantification Analysis and State Space Divergence Reconstruction for Financial Time Series Analysis. Physica A: Statistical Mechanics and its Applications, 376, 487-499. doi:10.1016/j.physa.2006.10.020
  • Strozzi, F., Gutiérrez, E., Noè, C., Rossi, T., Serati, M., & Zaldívar, J. M. (2008). Measuring Volatility in the Nordic Spot Electricity Market Using Recurrence Quantification Analysis. European Physical Journal Special Topics, 164(1), 105-115. doi:10.1140/epjst/e2008-00837-1
  • Takens, F. (1981). Detecting Strange Attractors in Turbulence. In R. D. & Y. L.S. (Eds.), Dynamical Systems and Turbulence (pp. 366-381). Berlin, Heidelberg: Springer.
  • Tredinnick, L. (2019). Cryptocurrencies and the blockchain. Business Information Review, 36(1), 39-44.
  • Webber, C. L., Jr., & Zbilut, J. P. (1994). Dynamical Assessment of Physiological Systems and States Using Recurrence Plot Strategies. Journal of Applied Physiology, 76(2), 965-973. doi:10.1152/jappl.1994.76.2.965
  • Yuan, Y., & Wang, F. Y. (2018). Blockchain and cryptocurrencies: Model, techniques, and applications. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(9), 1421-1428.
  • Xing, Y., & Wang, J. (2020). Linkages between global crude oil market volatility and financial market by complexity synchronization. Empirical Economics, 59(5), 2405-2421. doi:10.1007/s00181-019-01762-w
  • Zbilut, J. P. (2005). Use of Recurrence Quantification Analysis in Economic Time Series. In M. Salzano & A. Kirman (Eds.), Economics: Complex Windows (pp. 91-104). Milano: Springer.
  • Zbilut, J. P., & Webber, C. L. (1992). Embeddings and Delays as Derived from Quantification of Recurrence Plots. Physics Letters A, 171(3-4), 199-203. doi:10.1016/0375-9601(92)90426-m

Details

Primary Language English
Subjects Economics, Business Finance
Journal Section Research Articles
Authors

Baki ÜNAL> (Primary Author)
ISKENDERUN TECHNICAL UNIVERSITY
0000-0001-9154-0931
Türkiye

Early Pub Date July 30, 2022
Publication Date July 30, 2022
Published in Issue Year 2022, Volume 4, Issue 2

Cite

APA Ünal, B. (2022). Stability Analysis of Bitcoin using Recurrence Quantification Analysis . Chaos Theory and Applications , 4 (2) , 104-110 . DOI: 10.51537/chaos.1112188

Chaos Theory and Applications in Applied Sciences and Engineering: An interdisciplinary journal of nonlinear science 23830