Skip to main content
SpringerLink
Log in

Regime switching and causal network analysis of cryptocurrency volatility: evidence from pre-COVID and post-COVID analysis

  • Original Article
  • Published:
Digital Finance Aims and scope Submit manuscript

Abstract

This paper investigates the volatility dynamics of Bitcoin, Ethereum, and Litecoin both before and after the COVID-19 pandemic. Employing an asymmetric two-state MS-MGARCH model, we identify a regime change in their volatility dynamics, providing a foundation for examining pre- and post-COVID volatility spillovers. Using the BEKK-GARCH model, we statistically confirm a spillover and quantify the correlation strength among the cryptocurrencies through the Windowed Scalogram Difference approach. Additionally, the cross-quantilogram approach is utilized to identify potential causal networks among the cryptocurrencies. The study unveils bidirectional shock transmission and volatility spillovers between Bitcoin and Ethereum, as well as Bitcoin and Litecoin, underscoring their interconnected nature. Furthermore, bidirectional volatility connections between Litecoin and Ethereum are uncovered. These findings enhance our comprehension of cryptocurrency volatility, emphasizing the influence of historical shocks and prior volatility on present dynamics. The study has practical implications for investors, traders, and policymakers, offering valuable insights for risk management in the cryptocurrency market, thus contributing to the advancement of knowledge in cryptocurrency dynamics and supporting more informed decision-making in this continually evolving financial landscape.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Availability of data and materials

Our data will be available on request.

References

  • Alam, M., Chowdhury, M. A. F., Abdullah, M., & Masih, M. (2023). Volatility spillover and connectedness among REITs, NFTs, cryptocurrencies and other assets: Portfolio implications. Investment Analysts Journal. https://doi.org/10.1080/10293523.2023.2179161

    Article  Google Scholar 

  • Almeida, J., & Gonçalves, T. C. (2022). A systematic literature review of volatility and risk management on cryptocurrency investment: A methodological point of view. Risks, 10(5), 107.

    Article  Google Scholar 

  • Ampountolas, A. (2022). Cryptocurrencies intraday high-frequency volatility spillover effects using univariate and multivariate garch models. International Journal of Financial Studies, 10(3), 51.

    Article  Google Scholar 

  • Apergis, N. (2022). COVID-19 and cryptocurrency volatility: Evidence from asymmetric modelling. Finance Research Letters, 47, 102659.

    Article  PubMed  Google Scholar 

  • Ardia, D. (2008). Financial risk management with Bayesian estimation of GARCH models (Vol. 612). Springer.

    Google Scholar 

  • Arouri, M. E. H., Jouini, J., & Nguyen, D. K. (2011). Volatility spillovers between oil prices and stock sector returns: Implications for portfolio management. Journal of International Money and Finance, 30(7), 1387–1405.

    Article  Google Scholar 

  • Aste, T. (2019). Cryptocurrency market structure: Connecting emotions and economics. Digital Finance, 1(1–4), 5–21.

    Article  Google Scholar 

  • Baba, Y., Engle, R. F., Kraft, D. F., & Kroner, K. F. (1990). Multivariate simultaneous generalized ARCH. University of California, San Diego, Department of Economics.

    Google Scholar 

  • Bollen, N. P. (1998). Valuing options in regime-switching models. Journal of Derivatives, 6, 38–50.

    Article  Google Scholar 

  • Bolós, V. J., Benítez, R., Ferrer, R., & Jammazi, R. (2017). The windowed scalogram difference: A novel wavelet tool for comparing time-series. Applied Mathematics and Computation, 312, 49–65.

    Article  MathSciNet  Google Scholar 

  • Bouri, E., Christou, C., & Gupta, R. (2022). Forecasting returns of major cryptocurrencies: Evidence from regime-switching factor models. Finance Research Letters, 49, 103193.

    Article  Google Scholar 

  • Bouri, E., Kamal, E., & Kinateder, H. (2023). FTX Collapse and systemic risk Spillovers from FTX Token to major cryptocurrencies. Finance Research Letters, 56, 104099.

    Article  Google Scholar 

  • Bouteska, A., Sharif, T., & Abedin, M. Z. (2023). COVID-19 and stock returns: Evidence from the Markov switching dependence approach. Research in International Business and Finance, 64, 101882.

    Article  PubMed  PubMed Central  Google Scholar 

  • Catania, L., Grassi, S., & Ravazzolo, F. (2018). Predicting the volatility of cryptocurrency time-series. Mathematical and Statistical Methods for Actuarial Sciences and Finance: MAF, 2018, 203–207.

    Article  Google Scholar 

  • Chkili, W. (2021). Modeling Bitcoin price volatility: Long memory vs Markov switching. Eurasian Economic Review, 11, 433–448.

    Article  Google Scholar 

  • Choudhry, T., & Wu, H. (2008). Forecasting ability of GARCH vs Kalman filter method: Evidence from daily UK time-varying beta. Journal of Forecasting, 27(8), 670–689.

    Article  MathSciNet  Google Scholar 

  • 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.

    Article  Google Scholar 

  • D’Amato, V., Levantesi, S., & Piscopo, G. (2022). Deep learning in predicting cryptocurrency volatility. Physica a: Statistical Mechanics and Its Applications, 596, 127158.

    Article  Google Scholar 

  • Das, D., Kayal, P., & Maiti, M. (2023). A K-means clustering model for analyzing the Bitcoin extreme value returns. Decision Analytics Journal, 6, 100152.

    Article  Google Scholar 

  • Delfabbro, P., King, D. L., & Williams, J. (2021). The psychology of cryptocurrency trading: Risk and protective factors. Journal of Behavioral Addictions, 10(2), 201–207.

    Article  PubMed  PubMed Central  Google Scholar 

  • Engle, R. F., & Kroner, K. F. (1995). Multivariate simultaneous generalized ARCH. Econometric Theory, 11(1), 122–150.

    Article  MathSciNet  Google Scholar 

  • Fasanya, I. O., Oyewole, O. J., & Oliyide, J. A. (2022). Investors’ sentiments and the dynamic connectedness between cryptocurrency and precious metals markets. The Quarterly Review of Economics and Finance, 86, 347–364.

    Article  Google Scholar 

  • Fry, J., & Cheah, E. T. (2016). Negative bubbles and shocks in cryptocurrency markets. International Review of Financial Analysis, 47, 343–352.

    Article  Google Scholar 

  • Ftiti, Z., Louhichi, W., & Ben Ameur, H. (2023). Cryptocurrency volatility forecasting: What can we learn from the first wave of the COVID-19 outbreak?. Annals of Operations Research, 330(1):665–690.

    Article  MathSciNet  Google Scholar 

  • Fung, K., Jeong, J., & Pereira, J. (2022). More to cryptos than bitcoin: A GARCH modelling of heterogeneous cryptocurrencies. Finance Research Letters, 47, 102544.

    Article  Google Scholar 

  • Ghorbel, A., & Jeribi, A. (2021). Contagion of COVID-19 pandemic between oil and financial assets: The evidence of multivariate Markov switching GARCH models. Journal of Investment Compliance, 22(2), 151–169.

    Article  Google Scholar 

  • Haas, M., Mittnik, S., & Paolella, M. S. (2004). A new approach to Markov-switching GARCH models. Journal of Financial Econometrics, 2(4), 493–530.

    Article  Google Scholar 

  • Hamilton, J. D. (2010). Regime switching models. Macroeconometrics and time-series analysis, 202–209.

  • Hilebrand, E. (2005). Neglecting parameter changes in GARCH models. Journal of Econometrics, 129, 121–138.

    Article  MathSciNet  Google Scholar 

  • Hsu, S. H. (2022). Investigating the co-volatility spillover effects between cryptocurrencies and currencies at different natures of risk events. Journal of Risk and Financial Management, 15(9), 372.

    Article  Google Scholar 

  • Jiang, K., Zeng, L., Song, J., & Liu, Y. (2022). Forecasting value-at-risk of cryptocurrencies using the time-varying mixture-accelerating generalized autoregressive score model. Research in International Business and Finance, 61, 101634.

    Article  Google Scholar 

  • Katsiampa, P. (2019). Volatility co-movement between bitcoin and ether. Finance Research Letters, 30, 221–227.

    Article  Google Scholar 

  • Kayal, P., & Balasubramanian, G. (2021). Excess volatility in bitcoin: Extreme value volatility estimation. IIM Kozhikode Society & Management Review, 10(2), 222–231.

    Article  Google Scholar 

  • Kayal, P., & Rohilla, P. (2021). Bitcoin in the economics and finance literature: A survey. SN Business & Economics, 1(7), 88.

    Article  Google Scholar 

  • Klaassen, F. (2002). Improving GARCH volatility forecasts with regime-switching GARCH (pp. 223–254). Physica-Verlag HD.

    Google Scholar 

  • Kyriazis, N. A. (2019). A survey on empirical findings about spillovers in cryptocurrency markets. Journal of Risk and Financial Management, 12(4), 170.

    Article  Google Scholar 

  • Labidi, C., Rahman, M. L., Hedström, A., Uddin, G. S., & Bekiros, S. (2018). Quantile dependence between developed and emerging stock markets aftermath of the global financial crisis. International Review of Financial Analysis, 59, 179–211.

    Article  Google Scholar 

  • Ma, F., Liang, C., Ma, Y., & Wahab, M. I. M. (2020). Cryptocurrency volatility forecasting: A Markov regime-switching MIDAS approach. Journal of Forecasting, 39(8), 1277–1290.

    Article  MathSciNet  Google Scholar 

  • Madichie, C. V., Ngwu, F. N., Eze, E. A., & Maduka, O. D. (2023). Modelling the dynamics of cryptocurrency prices for risk hedging: The case of Bitcoin, Ethereum, and Litecoin. Cogent Economics & Finance, 11(1), 2196852.

    Article  Google Scholar 

  • Mensi, W., Gubareva, M., Ko, H. U., Vo, X. V., & Kang, S. H. (2023). Tail spillover effects between cryptocurrencies and uncertainty in the gold, oil, and stock markets. Financial Innovation, 9(1), 1–27.

    Article  Google Scholar 

  • Mgadmi, N., Béjaoui, A., & Moussa, W. (2023). Disentangling the nonlinearity effect in cryptocurrency markets during the covid-19 pandemic: Evidence from a regime-switching approach. Asia-Pacific Financial Markets, 30(3), 457–473.

    Article  Google Scholar 

  • Mikosch, T., & Starica, C. (2004). Nonstationarities in financial time-series, the long-range dependence, and the iGARCH effects. Review of Economics and Statistics, 86, 378–390.

    Article  Google Scholar 

  • Mullen, K., Ardia, D., Gil, D. L., Windover, D., & Cline, J. (2011). DEoptim: An R package for global optimization by differential evolution. Journal of Statistical Software, 40(6), 1–26.

    Article  Google Scholar 

  • Murty, S., Victor, V., & Fekete-Farkas, M. (2022). Is Bitcoin a Safe Haven for Indian Investors? A GARCH Volatility Analysis. Journal of Risk and Financial Management, 15(7), 317.

    Article  Google Scholar 

  • Nair, J. V., & Kayal, P. (2022). A study of tail-risk spillovers in cryptocurrency markets. Global Business Review. https://doi.org/10.1177/09721509221079969

    Article  Google Scholar 

  • Özdemir, O. (2022). Cue the volatility spillover in the cryptocurrency markets during the COVID-19 pandemic: Evidence from DCC-GARCH and wavelet analysis. Financial Innovation, 8(1), 1–38.

    Article  MathSciNet  Google Scholar 

  • Ranjan, S., Kayal, P., & Saraf, M. (2023). Bitcoin price prediction: A machine learning sample dimension approach. Computational Economics, 61(4), 1617–1636.

    Article  Google Scholar 

  • Sajeev, K. C., & Afjal, M. (2022). Contagion effect of cryptocurrency on the securities market: A study of Bitcoin volatility using diagonal BEKK and DCC GARCH models. SN Business & Economics, 2(6), 57.

    Article  Google Scholar 

  • Shahzad, S. J. H., Bouri, E., Ahmad, T., & Naeem, M. A. (2022). Extreme tail network analysis of cryptocurrencies and trading strategies. Finance Research Letters, 44, 102106.

    Article  Google Scholar 

  • Sharma, G., Kayal, P., & Pandey, P. (2019). Information linkages among BRICS countries: Empirical evidence from implied volatility indices. Journal of Emerging Market Finance, 18(3), 263–289.

    Article  Google Scholar 

  • Smales, L. A. (2021). Volatility spillovers among cryptocurrencies. Journal of Risk and Financial Management, 14(10), 493.

    Article  Google Scholar 

  • Som, A., & Kayal, P. (2022). A multicountry comparison of cryptocurrency vs gold: Portfolio optimization through generalized simulated annealing. Blockchain: Research and Applications, 3(3), 100075.

    Google Scholar 

  • Varma, Y., Venkataramani, R., Kayal, P., & Maiti, M. (2021). Short-term impact of COVID-19 on Indian stock market. Journal of Risk and Financial Management, 14(11), 558.

    Article  Google Scholar 

  • Verma, T., & Pearl, J. (1990). Causal networks: Semantics and expressiveness. In Machine intelligence and pattern recognition (Vol. 9, pp. 69–76). North-Holland.

  • Vo, X. V., & Ellis, C. (2018). International financial integration: Stock return linkages and volatility transmission between Vietnam and advanced countries. Emerging Markets Review, 36, 19–27.

    Article  Google Scholar 

  • Wan, Y., Song, Y., Zhang, X., & Yin, Z. (2023). Asymmetric volatility connectedness between cryptocurrencies and energy: Dynamics and determinants. Frontiers in Environmental Science, 11, 104.

    Article  Google Scholar 

  • Yousaf, I., & Ali, S. (2021). Linkages between stock and cryptocurrency markets during the COVID-19 outbreak: An intraday analysis. The Singapore Economic Review. https://doi.org/10.1142/S0217590821470019

    Article  Google Scholar 

  • Yu, L., Zha, R., Stafylas, D., He, K., & Liu, J. (2020). Dependences and volatility spillovers between the oil and stock markets: New evidence from the copula and VAR-BEKK-GARCH models. International Review of Financial Analysis, 68, 101280.

    Article  Google Scholar 

  • Zhang, L., Bouri, E., & Chen, Y. (2023). Co-jump dynamicity in the cryptocurrency market: A network modelling perspective. Finance Research Letters, 58, 104372.

    Article  Google Scholar 

Download references

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Madras School of Economics (MSE), Behind Government Data Centre, Gandhi Mandapam Road, Kottur, Chennai, 600025, India

    Parthajit Kayal

  2. Indian Institute of Science, CV Raman Rd, Bengaluru, Karnataka, 560012, India

    Sumanjay Dutta

Authors
  1. Parthajit Kayal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sumanjay Dutta
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors read and approved the final manuscript.

Corresponding author

Correspondence to Parthajit Kayal.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kayal, P., Dutta, S. Regime switching and causal network analysis of cryptocurrency volatility: evidence from pre-COVID and post-COVID analysis. Digit Finance (2024). https://doi.org/10.1007/s42521-023-00104-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42521-023-00104-x

Keywords

JEL Classification

Search

Navigation