Cryptocurrency Crises: The Role of Sentiment, Financial Stress, and Economic Policy Uncertainty
DOI:
https://doi.org/10.47743/saeb-2026-0015Keywords:
cryptocurrency crises, Bitcoin; Ethereum, Ripple, Google Trends, Financial Stress Index, Economic Policy Uncertainty.Abstract
This paper investigates the economic and behavioral determinants of crises in three major cryptocurrencies: Bitcoin, Ethereum, and Ripple. It focuses on the impact of key factors such as returns, volatility, investor sentiment, the Financial Stress Index (FSI), and Economic Policy Uncertainty (EPU). Crises are identified using the CMAX method, while their determinants are analyzed using both probit and logit models. The analysis identifies three major crises for Bitcoin – linked to the European sovereign debt crisis, the collapse of Mt. Gox, and the COVID-19 pandemic – along with a prolonged crisis in Ethereum from January 2018 to January 2021, and a persistent crisis in Ripple with no observed recovery. Results show that higher returns significantly reduce the likelihood of crises across all three cryptocurrencies, while increased volatility consistently raises crisis probability, reflecting heightened market uncertainty and risk aversion. Investor sentiment, measured through Google Trends, shows asset-specific effects: both optimistic and pessimistic sentiment increase crisis risk for Bitcoin and Ripple, while only pessimistic sentiment significantly affects Ethereum. Additionally, both FSI and EPU are positively and significantly associated with crisis occurrence, underscoring the influence of macro-financial stress and policy uncertainty on cryptocurrency stability.
References
Aggarwal, R., Inclan, C., & Leal, R. (1999). Volatility in Emerging Stock Markets. Journal of Financial and Quantitative Analysis, 34(1), 33–55. http://dx.doi.org/10.2307/2676245
Akyildirim, E., Aysan, A. F., Cepni, O., & Darendeli, S. P. C. (2021). Do Investor Sentiments Drive Cryptocurrency Prices? Economics Letters, 206(September), 1–6. http://dx.doi.org/10.1016/j.econlet.2021.109980
Al-Shboul, M., Assaf, A., & Mokni, K. (2023). Does Economic Policy Uncertainty Drive the Dynamic Spillover Among Traditional Currencies and Cryptocurrencies? The Role of the COVID-19 Pandemic. Research in International Business and Finance, 64(January), 1–27. http://dx.doi.org/10.1016/j.ribaf.2022.101824
Anamika, A., & Subramaniam, S. (2022). Do News Headlines Matter in the Cryptocurrency Market? Applied Economics, 54(54), 6322–6338. http://dx.doi.org/10.1080/00036846.2022.2061904
Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring Economic Policy Uncertainty. The Quarterly Journal of Economics, 131(4), 1593–1636. http://dx.doi.org/10.1093/qje/qjw024
Baur, D. G., Hong, K., & Lee, A. D. (2018). Bitcoin: Medium of Exchange or Speculative Assets? Journal of International Financial Markets, Institutions and Money, 54(May), 177–189. http://dx.doi.org/10.1016/j.intfin.2017.12.004
Ben Yaala, S., & Henchiri, J. E. (2024a). Predicting Stock Market Crashes in MENA Regions: Study Based on the Irrationality of Investor Behavior and the NARX Model. Journal of Financial Regulation and Compliance, 32(5), 590–619. http://dx.doi.org/10.1108/JFRC-12-2023-0201
Ben Yaala, S., & Henchiri, J. E. (2024b). Predicting Stock Market Crashes on the African Stock Markets : Evidence from Log-Periodic Power Law Model. African Journal of Economic and Management Studies, 15(3), 380–401. http://dx.doi.org/10.1108/AJEMS-03-2023-0113
Ben Yaala, S., & Henchiri, J. E. (2025a). Climate Risks and Cryptocurrency Volatility : Evidence from Crypto Market Crisis. China Finance Review International, 15(3), 636–667. http://dx.doi.org/10.1108/CFRI-09-2024-0575
Ben Yaala, S., & Henchiri, J. E. (2025b). Detecting and Analyzing Explosive Bubbles and their Relationship with Volatility: Evidence from Tunisia. RAUSP Management Journal, 60(1), 92–107. http://dx.doi.org/10.1108/RAUSP-05-2024-0097
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. http://dx.doi.org/10.1016/0304-4076(86)90063-1
Borio, C., & Lowe, P. (2002). Assessing the Risk of Banking Crises. BIS Quarterly Review, 7(1), 43–54.
Boucher, C. (2004). Identification et comparaison des crises boursieres. In R. Boyer, M. Dehove, & D. Plihon (Eds.), Les crises financieres (Vol. 50, pp. 375–396): Conseil d’Analyse Economique.
Bouri, E., Gabauer, D., Gupta, R., & Tiwari, A. K. (2021). Volatility Connectedness of Major Cryptocurrencies: The Role of Investor Happiness. Journal of Behavioral and Experimental Finance, 30(June), 1–11. http://dx.doi.org/10.1016/j.jbef.2021.100463
Bouri, E., Gupta, R., Lau, C. K. M., Roubaud, D., & Wang, S. (2018). Bitcoin and Global Financial Stress: A Copula-Based Approach to Dependence and Causality in the Quantiles. The Quarterly Review of Economics and Finance, 69(2), 297–307. http://dx.doi.org/10.1016/j.qref.2018.04.003
Bouri, E., Shahzad, S. J. H., & Roubaud, D. (2019). Co-Explosivity in the Cryptocurrency Market. Finance Research Letters, 29(June), 178–183. http://dx.doi.org/10.1016/j.frl.2018.07.005
Bouteska, A., Mefteh-Wali, S., & Dang, T. (2022). Predictive power of investor sentiment for Bitcoin returns: Evidence from COVID-19 pandemic. Technological Forecasting and Social Change, 184, 121999. http://dx.doi.org/10.1016/j.techfore.2022.121999
Choudhry, T. (1996). Stock Market Volatility and the Crash of 1987: Evidence from Six Emerging Markets. Journal of International Money and Finance, 15(6), 969–981. http://dx.doi.org/10.1016/S0261-5606(96)00036-8
Corbet, S., Lucey, B., & Yarovaya, L. (2018). Datestamping the Bitcoin and Ethereum Bubbles. Finance Research Letters, 26(September), 81–88. http://dx.doi.org/10.1016/j.frl.2017.12.006
Coudert, V., & Gex, M. (2008). Does risk aversion drive financial crises? Testing the predictive power of empirical indicators. Journal of Empirical Finance, 15(2), 167–184. http://dx.doi.org/https://doi.org/10.1016/j.jempfin.2007.06.001
Da, Z., Engelberg, J., & Gao, P. (2011). In Search of Attention. The Journal of Finance, 66(5), 1461–1499. http://dx.doi.org/10.1111/j.1540-6261.2011.01679.x
Da, Z., Engelberg, J., & Gao, P. (2015). The Sum of All FEARS Investor Sentiment and Asset Prices. The Review of Financial Studies, 28(1), 1–32. http://dx.doi.org/10.1093/rfs/hhu072
Demir, E., Gozgor, G., Lau, C. K. M., & Vigne, S. A. (2018). Does Economic Policy Uncertainty Predict the Bitcoin Returns? An Empirical Investigation. Finance Research Letters, 26(September), 145–149. http://dx.doi.org/10.1016/j.frl.2018.01.005
Fang, W. (2001). Stock Return Process and Expected Depreciation over the Asian Financial Crisis. Applied Economics, 33(7), 905–912. http://dx.doi.org/10.1080/00036840122487
García Petit, J. J., Vaquero Lafuente, E., & Rúa Vieites, A. (2019). How information technologies shape investor sentiment: A web-based investor sentiment index. Borsa Istanbul Review, 19(2), 95–105. http://dx.doi.org/https://doi.org/10.1016/j.bir.2019.01.001
Güler, D. (2023). The Impact of Investor Sentiment on Bitcoin Returns and Conditional Volatilities during the Era of Covid-19. Journal of Behavioral Finance, 24(3), 276–289. http://dx.doi.org/10.1080/15427560.2021.1975285
Gurdgiev, C., & O’Loughlin, D. (2020). Herding and Anchoring in Cryptocurrency Markets: Investor Reaction to Fear and Uncertainty. Journal of Behavioral and Experimental Finance, 25(January), 1–10. http://dx.doi.org/10.1016/j.jbef.2020.100271
He, C., Li, Y., Wang, T., & Shah, S. A. (2024). Is Cryptocurrency a Hedging Tool during Economic Policy Uncertainty? An Empirical Investigation. Humanities & Social Sciences Communications, 11(January), 1–10. http://dx.doi.org/10.1057/s41599-023-02532-x
Huynh, T. L. D., Wang, M., & Vo, V. X. (2021). ECONOMIC POLICY UNCERTAINTY AND THE BITCOIN MARKET: AN INVESTIGATION IN THE COVID-19 PANDEMIC WITH TRANSFER ENTROPY. The Singapore Economic Review, 70(3), 647–673. http://dx.doi.org/10.1142/S0217590821500119
Kang, S. H., & Yoon, S. M. (2019). Dynamic Connectedness Network in Economic Policy Uncertainties. Applied Economics Letters, 26(1), 74–78. http://dx.doi.org/10.1080/13504851.2018.1438580
Katsiampa, P. (2019). An Empirical Investigation of Volatility Dynamics in the Cryptocurrency Market. Research in International Business and Finance, 50(December), 322–335. http://dx.doi.org/10.1016/j.ribaf.2019.06.004
Kurka, J. (2019). Do Cryptocurrencies and Traditional Asset Classes Influence Each Other? Finance Research Letters, 31(December ), 38–46. http://dx.doi.org/10.1016/j.frl.2019.04.018
Mokni, K. (2021). When, Where, and How Economic Policy Uncertainty Predicts Bitcoin Returns and Volatility? A Quantiles-based Analysis. The Quarterly Review of Economics and Finance, 80(May), 65–73. http://dx.doi.org/10.1016/j.qref.2021.01.017
Nasir, M. A., Huynh, T. L. D., Nguyen, S. P., & Duong, D. (2019). Forecasting Cryptocurrency Returns and Volume Using Search Engines. Financial Innovation, 5(2), 1–13. http://dx.doi.org/10.1186/s40854-018-0119-8
Nur, T., & Korkmaz, T. (2022). Volatility Spillover between Bitcoin and Financial Stress Index. Journal of Corporate Governance, Insurance and Risk Management, 9(1), 49–70. http://dx.doi.org/10.51410/jcgirm.9.1.4
Patel, S. A., & Sarkar, A. (1998). Crises in Developed and Emerging Stock Markets. Financial Analysts Journal, 54(6), 50–61. http://dx.doi.org/10.2469/faj.v54.n6.2225
Paule-Vianez, J., Prado-Román, C., & Gómez-Martínez, R. (2020). Economic Policy Uncertainty and Bitcoin. Is Bitcoin A Safe-Haven Asset? European Journal of Management and Business Economics, 29(3), 347–363. http://dx.doi.org/10.1108/EJMBE-07-2019-0116
Poyser, O. (2019). Exploring the Dynamics of Bitcoin’s Price: A Bayesian Structural Time Series Approach. Eurasian Economic Review, 9(March), 29–60. http://dx.doi.org/10.1007/s40822-018-0108-2
Salisu, A. A., Demirer, R., & Gupta, R. (2023). Policy uncertainty and stock market volatility revisited: The predictive role of signal quality. Journal of Forecasting, 42(8), 2307–2321. http://dx.doi.org/10.1002/for.3016
Sovbetov, Y. (2018). Factors Influencing Cryptocurrency Prices: Evidence from Bitcoin, Ethereum, Dash, Litcoin, and Monero. Journal of Economics and Financial Analysis, 2(2), 1–27. http://dx.doi.org/10.48550/arXiv.2511.22782
Sun, Y., Amanda, C., & Centana, B. C. (2023). The Effect of Hashrate, Transaction Volume, Social Media and Macroeconomics on Bitcoin before and During the COVID-19 Pandemic. Asian Journal of Accounting Research, 8(3), 293–306. http://dx.doi.org/10.1108/AJAR-10-2022-0319
Tiwari, A. K., Raheem, I. D., & Kang, S. H. (2019). Time-Varying Dynamic Conditional Correlation between Stock and Cryptocurrency Markets Using the Copula-ADCC-EGARCH Model. Physica A, 535(December), 1–9. http://dx.doi.org/10.1016/j.physa.2019.122295
Yen, K. C., & Cheng, H. P. (2021). Economic Policy Uncertainty and Cryptocurrency Volatility. Finance Research Letters, 38(January), 1–7. http://dx.doi.org/10.1016/j.frl.2020.101428
Yin, W., Chen, Z., Luo, X., & Kirkulak-Uludag, B. (2024). Forecasting Cryptocurrencies’ Price with the Financial Stress Index: A Graph Neural Network Prediction Strategy. Applied Economics Letters, 31(7), 630–639. http://dx.doi.org/10.1080/13504851.2022.2141436
Zhang, H., & Wang, P. (2021). Does Bitcoin or Gold React to Financial Stress Alike? Evidence from the US and China. International Review of Economics & Finance, 71(January ), 629–648. http://dx.doi.org/10.1016/j.iref.2020.10.007
Zouaoui, M., Nouyrigat, G., & Beer, F. (2011). How Does Investor Sentiment Affect Stock Market Crises? Evidence from Panel Data. Financial Review, 46(4), 723–747. http://dx.doi.org/10.1111/j.1540-6288.2011.00318.x
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Sirine Ben Yaala, Jamel Eddine Henchiri
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
All accepted papers are published on an Open Access basis.
The Open Access License is based on the Creative Commons license.
The non-commercial use of the article will be governed by the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License as currently displayed on https://creativecommons.org/licenses/by-nc-nd/4.0
Under the Creative Commons Attribution-NonCommercial-NoDerivatives license, the author(s) and users are free to share (copy, distribute and transmit the contribution) under the following conditions:
1. they must attribute the contribution in the manner specified by the author or licensor,
2. they may not use this contribution for commercial purposes,
3. they may not alter, transform, or build upon this work.
This work is licenced under a 
