Abstract
Bitcoin has gradually gained acceptance as a payment method that, unlike electronic payments in dollars or euros, passes through the international trading system with zero or lower fees. Moreover, Bitcoin and e-commerce have become increasingly intertwined in recent years as cryptocurrencies gain mainstream acceptance. In this paper, we analyze Bitcoin price evolution from September 2014 until July 2023, factors that influence price volatility and assess its future volatility using Autoregressive Conditional Heteroskedasticity (ARCH) models that predict the volatility of financial returns to conceive strategies for merchants that accept Bitcoin as a payment option. The Generalized ARCH model (GARCH) extends the model to capture more persistent volatility patterns. Further, we estimate symmetric and asymmetric GARCH (1,1)-type models with normal and non-normal innovations. The best proved to be EGARCH (1,1) with t-distribution innovation. To assist merchants in making decisions regarding Bitcoin adoption, two concepts are relevant: the EGARCH model and VaR. EGARCH model is used to forecast the volatility of the financial asset, while VaR is a widely used risk management tool that estimates the potential loss in value of a portfolio over a defined period. For a merchant holding Bitcoin, VaR assists in understanding the maximum expected loss over a certain time frame with a certain level of confidence (like 95% or 99%). The results show that a VaR coverage of 0.044 at a 5% probability level suggests that there is 95% confidence that the maximum loss will not exceed 4.4% of the investment value.
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References
Wonglimpiyarat, J. (2015). The new Darwinism of the payment system: Will Bitcoin replace our cash-based society? Journal of Internet Banking and Commerce. https://doi.org/10.4172/1204-5357.S2-002
Bourghelle, D., Jawadi, F., & Rozin, P. (2022). Do collective emotions drive bitcoin volatility? A triple regime-switching vector approach. Journal of Economic Behavior & Organization. https://doi.org/10.1016/j.jebo.2022.01.026
Sapkota, N. (2022). News-based sentiment and bitcoin volatility. International Review of Financial Analysis. https://doi.org/10.1016/j.irfa.2022.102183
Mattke, J., Maier, C., Reis, L., & Weitzel, T. (2019). Bitcoin investment: A mixed methods study of investment motivations. European Journal of Information Systems. https://doi.org/10.1080/0960085X.2020.1787109
Dutta, A., Das, D., Jana, R. K., & Vo, X. V. (2020). COVID-19 and oil market crash: Revisiting the safe haven property of gold and Bitcoin. Resources Policy. https://doi.org/10.1016/j.resourpol.2020.101816
Erdin, E., Cebe, M., Akkaya, K., Solak, S., Bulut, E., & Uluagac, S. (2020). A Bitcoin payment network with reduced transaction fees and confirmation times. Computing Networks. https://doi.org/10.1016/j.comnet.2020.107098
Divakaruni, A., & Zimmerman, P. (2023). The lightning network: Turning bitcoin into money. Finance Research Letters. https://doi.org/10.1016/j.frl.2022.103480
Hannon, C., & Jin, D. (2019). Bitcoin payment-channels for resource limited IoT devices. ACM Int. Conf. Proceeding Ser. https://doi.org/10.1145/3312614.3312629
Mensah, I. K., & Mwakapesa, D. S. (2022). The drivers of the behavioral adoption intention of BITCOIN Payment from the perspective of Chinese citizens. Secur. Commun. Networks. https://doi.org/10.1155/2022/7373658
McGinn, D., McIlwraith, D., & Guo, Y. (2018). Towards open data blockchain analytics: A bitcoin perspective. R. Soc. Open Sci. https://doi.org/10.1098/rsos.180298
Nerurkar, P., Patel, D., Busnel, Y., Ludinard, R., Kumari, S., & Khan, M. K. (2021). Dissecting bitcoin blockchain: Empirical analysis of bitcoin network (2009–2020). Journal of Network and Computer Applications. https://doi.org/10.1016/j.jnca.2020.102940
Kher, R., Terjesen, S., & Liu, C. (2021). Blockchain, Bitcoin, and ICOs: A review and research agenda. Small Business Economics. https://doi.org/10.1007/s11187-019-00286-y
Mjoska, M., Ristevski, B., Savoska, S., Trajkovik, V. Predicting Bitcoin Volatility Using Machine Learning Algorithms and Blockchain Technology, in: CEUR Workshop Proc., 2022
Loh, E. C., Ismail, S., Khamis, A., & Mustapha, A. (2020). Comparison of feedforward neural network with different training algorithms for bitcoin price forecasting. ASM Science Journal. https://doi.org/10.32802/asmscj.2020.sm26
Huberman, G., Leshno, J. D., & Moallemi, C. (2021). Monopoly without a monopolist: An economic analysis of the bitcoin payment system. Review of Economic Studies. https://doi.org/10.1093/restud/rdab014
Luther, W. J., & Stein Smith, S. (2020). Is Bitcoin a decentralized payment mechanism. Journal of Institutional Economics. https://doi.org/10.1017/S1744137420000107
Al-Haija, Q. A., & Alsulami, A. A. (2021). High performance classification model to identify ransomware payments for heterogeneous bitcoin networks. Electron. https://doi.org/10.3390/electronics10172113
Paquet-Clouston, M., Haslhofer, B., & Dupont, B. (2019). Ransomware payments in the Bitcoin ecosystem. Journal of Cybersecurity. https://doi.org/10.1093/cybsec/tyz003
Ciaian, P., D’artis, K., & Rajcaniova, M. (2021). The economic dependency of bitcoin security. Applied Economics. https://doi.org/10.1080/00036846.2021.1931003
Bergsli, L. Ø., Lind, A. F., Molnár, P., & Polasik, M. (2022). Forecasting volatility of Bitcoin. Research in International Business and Finance. https://doi.org/10.1016/j.ribaf.2021.101540
Longo, R., Podda, A. S., & Saia, R. (2020). Analysis of a consensus protocol for extending consistent subchains on the bitcoin blockchain. Computation. https://doi.org/10.3390/COMPUTATION8030067
Swammy, S., Thompson, R., & Loh, M. (2019). Tales from the Crypt: The dawn of crypto currency. Crypto Uncovered The Evolution of Bitcoin and the Crypto Currency Marketplace. https://doi.org/10.1007/978-3-030-00135-3_2
Hedman, J., Beaulieu, T., & Karlström, M. (2021). The tales of alphanumerical symbols in media: The case of bitcoin. Journal of Theoretical and Applied Electronic Commerce Research. https://doi.org/10.3390/jtaer16070152
López-Cabarcos, M. Á., Pérez-Pico, A. M., Piñeiro-Chousa, J., & Šević, A. (2021). Bitcoin volatility, stock market and investor sentiment Are they connected? Finance Research Letters. https://doi.org/10.1016/j.frl.2019.101399
Ayboğa, M. H., & Ganii, F. (2022). The Covid 19 crisis and the future of bitcoin in E-commerce. Journal Organization Behavior Research. https://doi.org/10.51847/hta7jg55of
Marecki, K., & Wójcik-Czerniawska, A. (2020). Cryptocurrency market of bitcoin and payment acceptability in E-commerce. Economy Business Journal., 14(1), 257–267.
Mnif, E., & Jarboui, A. (2021). COVID-19, bitcoin market efficiency, herd behaviour. Review of Behavioural Finance. https://doi.org/10.1108/RBF-09-2020-0233
Hou, J. P., Liu, J., & Jie, Y. J. (2021). Examining the psychological state analysis relationship between bitcoin prices and COVID-19. Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2021.647691
Ahn, J., Park, M., Shin, H., & Paek, J. (2019). A model for deriving trust and reputation on blockchain-based e-payment system. Applied Sciences. https://doi.org/10.3390/app9245362
Özyılmaz, K.R., Kongel, N.B., Nalbant, A.E. and Özcan, A., 2019. A Multi-protocol Payment System to Facilitate Financial Inclusion. In Data Privacy Management, Cryptocurrencies and Blockchain Technology: ESORICS 2019 International Workshops, DPM 2019 and CBT 2019, Luxembourg, September 26–27, 2019, Proceedings 14 (pp. 321-335). Springer International Publishing.
Abdulhakeem, S. A., & Hu, Q. (2021). Powered by blockchain technology, DeFi (decentralized finance) strives to increase financial inclusion of the unbanked by reshaping the world financial system. Modern Economy. https://doi.org/10.4236/me.2021.121001
Kayral, I. E., Jeribi, A., & Loukil, S. (2023). Are bitcoin and gold a safe haven during COVID-19 and the 2022 Russia-Ukraine War? J. Risk Financ. Manag. https://doi.org/10.3390/jrfm16040222
Zhang, Y., He, M., Wen, D., & Wang, Y. (2022). Forecasting bitcoin volatility: A new insight from the threshold regression model. Journal of Forecasting. https://doi.org/10.1002/for.2822
Hackethal, A., Hanspal, T., Lammer, D. M., & Rink, K. (2022). The Characteristics and portfolio behavior of bitcoin investors: evidence from indirect cryptocurrency investments. Rev. Financ. https://doi.org/10.1093/rof/rfab034
Diaconaşu, D. E., Mehdian, S., & Stoica, O. (2022). An analysis of investors’ behavior in Bitcoin market. PLoS ONE. https://doi.org/10.1371/journal.pone.0264522
Zhu, P., Zhang, X., Wu, Y., Zheng, H., & Zhang, Y. (2021). Investor attention and cryptocurrency: Evidence from the Bitcoin market. PLoS ONE. https://doi.org/10.1371/journal.pone.0246331
Tang, T., & Wang, Y. (2022). Liquidity shocks, price volatilities, and risk-managed strategy: evidence from bitcoin and beyond. Journal of Multinational Financial Management. https://doi.org/10.1016/j.mulfin.2022.100729
Guesmi, K., Saadi, S., Abid, I., & Ftiti, Z. (2019). Portfolio diversification with virtual currency: Evidence from bitcoin. International Review of Financial Analysis. https://doi.org/10.1016/j.irfa.2018.03.004
Troster, V., Tiwari, A. K., Shahbaz, M., & Macedo, D. N. (2019). Bitcoin returns and risk: A general GARCH and GAS analysis. Finance Research Letters. https://doi.org/10.1016/j.frl.2018.09.014
Lyócsa, Š, Molnár, P., Plíhal, T., & Širaňová, M. (2020). Impact of macroeconomic news, regulation and hacking exchange markets on the volatility of bitcoin. Journal of Economic Dynamics & Control. https://doi.org/10.1016/j.jedc.2020.103980
Shen, Z., Wan, Q., & Leatham, D. J. (2021). Bitcoin return volatility forecasting: A comparative study between GARCH and RNN. J. Risk Financ. Manag. https://doi.org/10.3390/jrfm14070337
Wu, C. C., Ho, S. L., & Wu, C. C. (2022). The determinants of Bitcoin returns and volatility: Perspectives on global and national economic policy uncertainty. Finance Research Letters. https://doi.org/10.1016/j.frl.2021.102175
Omura, A., Cheung, A., & Su, J. J. (2023). Does natural gas volatility affect Bitcoin volatility? Evidence from the HAR-RV model. Applied Economics. https://doi.org/10.1080/00036846.2023.2168608
Bakas, D., Magkonis, G., & Oh, E. Y. (2022). What drives volatility in Bitcoin market? Finance Research Letters. https://doi.org/10.1016/j.frl.2022.103237
Ben Nouir, J., & H. Ben Haj Hamida,. (2023). How do economic policy uncertainty and geopolitical risk drive Bitcoin volatility? Research in International Business and Finance. https://doi.org/10.1016/j.ribaf.2022.101809
Alqahtani, M., & Hu, M. (2020). Integrated energy scheduling and routing for a network of mobile prosumers. Energy. https://doi.org/10.1016/j.energy.2020.117451
Liang, C., Zhang, Y., Li, X., & Ma, F. (2022). Which predictor is more predictive for Bitcoin volatility? And why? International Journal of Finance and Economics. https://doi.org/10.1002/ijfe.2252
Anamika, M., & Chakraborty, S. (2023). Subramaniam, Does Sentiment Impact Cryptocurrency? Journal of Behavioral Finance. https://doi.org/10.1080/15427560.2021.1950723
Mohsin, M., Naseem, S., Ivașcu, L., Cioca, L. I., Sarfraz, M., & Stănică, N. C. (2021). Gauging the effect of investor sentiment on cryptocurrency market: an analysis of bitcoin currency. Romanian Jornal of Economic Forecasting., 24(4), 87.
Engle, R. F., & Ng, V. K. (1993). Measuring and Testing the Impact of News on Volatility. Journal of Finance. https://doi.org/10.2307/2329066
Jesika, S., Pratiwi, W., & Handani, D. (2023). Potential analysis of bitcoin cryptocurrency as a future investment asset: A systematic literature review. Open Access Indonesia Journal of Social Sciences, 6(4), 1010–1016.
Rudolf, K. O., El Zein, S. A., & Lansdowne, N. J. (2021). Bitcoin as an investment and hedge alternative. A dcc mgarch model analysis. Risks. https://doi.org/10.3390/risks9090154
Brik, H., El Ouakdi, J., & Ftiti, Z. (2022). Roles of stable versus nonstable cryptocurrencies in Bitcoin market dynamics. Research in International Business and Finance. https://doi.org/10.1016/j.ribaf.2022.101720
Attarzadeh, A., & Balcilar, M. (2022). On the dynamic return and volatility connectedness of cryptocurrency, crude oil, clean energy, and stock markets: A time-varying analysis. Environmental Science and Pollution Research. https://doi.org/10.1007/s11356-022-20115-2
Tang, C., & Liu, X. (2023). Bitcoin speculation, investor attention and major events. Are they connected? Applied Economics Letters. https://doi.org/10.1080/13504851.2022.2033677
Bouoiyour J., Selmi R., Tiwari A., Is Bitcoin business income or speculative bubble? Unconditional vs conditional frequency domain analysis, Ann Financ Econ (2018)
Su, C. W., Xi, Y., Tao, R., & Umar, M. (2022). Can bitcoin be a safe haven in fear sentiment? Technological and Economic Development of Economy. https://doi.org/10.3846/tede.2022.15502
Li, Z. Z., Tao, R., Su, C. W., & Lobonţ, O. R. (2019). Does Bitcoin bubble burst? Quality & Quantity. https://doi.org/10.1007/s11135-018-0728-3
Kumari, V., Bala, P. K., & Chakraborty, S. (2023). An empirical study of user adoption of cryptocurrency using blockchain technology: analysing role of success factors like technology awareness and financial literacy. Journal of Theoretical and Applied Electronic Commerce Research, 18, 1580–1600. https://doi.org/10.3390/jtaer18030080
Grobys, K., Junttila, J., Kolari, J. W., & Sapkota, N. (2021). On the stability of stablecoins. Journal of Empirical Finance. https://doi.org/10.1016/j.jempfin.2021.09.002
Ante, L., Fiedler, I., & Strehle, E. (2021). The impact of transparent money flows: Effects of stablecoin transfers on the returns and trading volume of Bitcoin. Technol. Forecast. Soc. Change. https://doi.org/10.1016/j.techfore.2021.120851
Bojaj, M. M., Muhadinovic, M., Bracanovic, A., Mihailovic, A., Radulovic, M., Jolicic, I., Milosevic, I., & Milacic, V. (2022). Forecasting macroeconomic effects of stablecoin adoption: A Bayesian approach. Economic Modelling. https://doi.org/10.1016/j.econmod.2022.105792
Mukharil, A., & Hanifah, R. N. (2019). Bitcoin influence on E-commerce. IOP Conf. Ser. Mater. Sci. Eng. https://doi.org/10.1088/1757-899X/662/3/032037
Budree, A., & Nyathi, T. N. (2023). Can cryptocurrency be a payment method in a developing economy? The case of bitcoin in South Africa. Journal of Electronic Commerce in Organizations. https://doi.org/10.4018/JECO.320223
Dewi, I. A., Miftahuddin, Y., Fattah, M. A., Palenda, C. B., & Erawan, S. F. (2021). Point of Sales System in InHome Café Website using Agile Methodology. Journal of Innovation and Community Engagement. https://doi.org/10.28932/jice.v1i1.3321
Manan, W. D. W. A., & Ridzwian, A. A. B. M. (2019). A point-of-sale system for measuring sales performance. International Journal of Advanced Trends Computer Science Engineering. https://doi.org/10.30534/ijatcse/2019/3081.52019
Bensona, S., Prasetya, F. H., & Harnadi, B. (2022). Implementation of Qr-code based point of sales application for retail store. Journal of Busines and Technology. https://doi.org/10.24167/jbt.v2i2.4395
C. Lu, G. Lauritano, J. Peltonen, CryptoKitties vs. Axie Infinity: Computational Analysis of NFT Game Reddit Discussions, in: Lect. Notes Inst. Comput. Sci. Soc. Telecommun. Eng. LNICST, 2023. https://doi.org/10.1007/978-3-031-28993-4_8.
Bezhovski, Z., Davcev, L., & Mitreva, M. (2021). Current adoption state of cryptocurrencies as an electronic payment method. Management Reseach and Practice., 13(1), 44–50.
Osman, S., Jabaruddin, N., Zon, A. S., Jifridin, A. A., & Zolkepli, A. K. (2021). Factors influencing the use of E-wallet among millennium tourist. Journal of Information Technology Management, 13(3), 70–81.
Tsang, K. P., & Yang, Z. (2021). The market for bitcoin transactions. Journal of International Financial Markets. https://doi.org/10.1016/j.intfin.2021.101282
Yu, M. (2019). Forecasting Bitcoin volatility: The role of leverage effect and uncertainty. Physica A: Statistical Mechanics and Its Applications. https://doi.org/10.1016/j.physa.2019.03.072
Dias, I. K., Fernando, J. M. R., & Fernando, P. N. D. (2022). Does investor sentiment predict bitcoin return and volatility? A quantile regression approach. International Review of Financial Analysis. https://doi.org/10.1016/j.irfa.2022.102383
Mokni, K., Bouteska, A., & Nakhli, M. S. (2022). Investor sentiment and Bitcoin relationship: A quantile-based analysis. The North American Journal of Economics and Finance. https://doi.org/10.1016/j.najef.2022.101657
Eom, C., Kaizoji, T., Kang, S. H., & Pichl, L. (2019). Bitcoin and investor sentiment: Statistical characteristics and predictability. Physica A: Statistical Mechanics and Its Applications. https://doi.org/10.1016/j.physa.2018.09.063
Guizani, S., & Nafti, I. K. (2019). The Determinants of bitcoin price volatility: An investigation with ARDL Model. Procedia Computer Science. https://doi.org/10.1016/j.procs.2019.12.177
Pichl, L., & Kaizoji, T. (2017). Volatility analysis of bitcoin price time series. Quantitative Finance and Economics. https://doi.org/10.3934/qfe.2017.4.474
J. Wang, Y. Xue, M. Liu, An Analysis of Bitcoin Price Based on VEC Model, in: 2016. https://doi.org/10.2991/icemi-16.2016.36.
Naimy, V. Y., & Hayek, M. R. (2018). Modelling and predicting the Bitcoin volatility using GARCH models. International Journal of Mathematical Modelling and Numerical. https://doi.org/10.1504/IJMMNO.2018.088994
Naimy, V., Haddad, O., Fernández-Avilés, G., & El Khoury, R. (2021). The predictive capacity of GARCH-type models in measuring the volatility of crypto and world currencies. PLoS ONE. https://doi.org/10.1371/journal.pone.0245904
Chu, J., Chan, S., Nadarajah, S., & Osterrieder, J. (2017). GARCH Modelling of Cryptocurrencies. Journal of Risk and Financial Management. https://doi.org/10.3390/jrfm10040017
Katsiampa, P. (2017). Volatility estimation for Bitcoin: A comparison of GARCH models. Economic Letters. https://doi.org/10.1016/j.econlet.2017.06.023
De Nicola, G. (2021). On the intraday behavior of bitcoin. Ledger. https://doi.org/10.5195/ledger.2021.213
Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica. https://doi.org/10.2307/1912773
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Economics. https://doi.org/10.1016/0304-4076(86)90063-1
Hansen, P. R., & Lunde, A. (2005). A forecast comparison of volatility models: Does anything beat a GARCH(1,1)? Journal of Applied Economics. https://doi.org/10.1002/jae.800
Wang, P. (2005). Financial Econometrics. Routledge. https://doi.org/10.4324/9780203990735
Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica. https://doi.org/10.2307/2938260
Tsay, R. S. (2010). Analysis of financial time series. Wiley. https://doi.org/10.1002/9780470644560
Bollerslev, T., Russell, J. R., & Watson, M. W. (2010). Volatility and Time Series Econometrics: Essays in Honor of Robert Engle. OUP oxford. https://doi.org/10.1093/acprof:oso/9780199549498.001.0001
Zakoian, J. M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics & Control. https://doi.org/10.1016/0165-1889(94)90039-6
Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of American Statistical Association. https://doi.org/10.2307/2286348
Stoimenov, P. (2011). Philippe Jorion, Value at Risk, 3rd Ed: The New Benchmark for Managing Financial Risk. Statistical Papers. https://doi.org/10.1007/s00362-009-0296-7
Brauneis, A., Mestel, R., Riordan, R., & Theissen, E. (2022). Bitcoin unchained: Determinants of cryptocurrency exchange liquidity. Journal of Empirical Finance. https://doi.org/10.1016/j.jempfin.2022.08.004
Vo, A., Chapman, T. A., & Lee, Y. S. (2022). Examining bitcoin and economic determinants: An evolutionary perspective. The Journal of Computer Information Systems. https://doi.org/10.1080/08874417.2020.1865851
Sarkodie, S. A., Ahmed, M. Y., & Leirvik, T. (2022). Trade volume affects bitcoin energy consumption and carbon footprint. Finance Research Letters. https://doi.org/10.1016/j.frl.2022.102977
Chen, W., Xu, H., Jia, L., & Gao, Y. (2021). Machine learning model for Bitcoin exchange rate prediction using economic and technology determinants. International Journal of Forecasting. https://doi.org/10.1016/j.ijforecast.2020.02.008
Su, X., & Li, Y. (2020). Dynamic sentiment spillovers among crude oil, gold, and Bitcoin markets: Evidence from time and frequency domain analyses. PLoS ONE. https://doi.org/10.1371/journal.pone.0242515
Huynh, T. L. D. (2023). When Elon Musk changes his tone, does bitcoin adjust its tune? Computational Economics. https://doi.org/10.1007/s10614-021-10230-6
Suardi, S., Rasel, A. R., & Liu, B. (2022). On the predictive power of tweet sentiments and attention on bitcoin. International Review of Economics and Finance. https://doi.org/10.1016/j.iref.2022.02.017
Fakharchian, S. (2023). Designing a forecasting assistant of the Bitcoin price based on deep learning using market sentiment analysis and multiple feature extraction. Soft Computing. https://doi.org/10.1007/s00500-023-09028-5
Akyildirim, E., Corbet, S., Katsiampa, P., Kellard, N., & Sensoy, A. (2020). The development of Bitcoin futures: Exploring the interactions between cryptocurrency derivatives. Finance Research Letters. https://doi.org/10.1016/j.frl.2019.07.007
Yi, E., Yang, B., Jeong, M., Sohn, S., & Ahn, K. (2023). Market efficiency of cryptocurrency: Evidence from the Bitcoin market. Science and Reports. https://doi.org/10.1038/s41598-023-31618-4
Biju, A. V., Mathew, A. M., Nithi Krishna, P. P., & Akhil, M. P. (2022). Is the future of bitcoin safe? A triangulation approach in the reality of BTC market through a sentiments analysis. Digital Finance. https://doi.org/10.1007/s42521-022-00052-y
Aivaz, K.-A., Munteanu, I. F., & Jakubowicz, F. V. (2023). Bitcoin in conventional markets: A study on blockchain-induced reliability investment slopes financial and accounting aspects. Mathematics. https://doi.org/10.3390/math11214508
Roozkhosh, P., & Pooya, A. (2023). Dynamic analysis of bitcoin price under market news and sentiments and government support policies. Computational Economics. https://doi.org/10.1007/s10614-023-10477-1
Abdalla, S. Z. S. (2012). Modelling exchange rate volatility using GARCH models: Empirical evidence from Arab countries. International Journal of Economics and Finance. https://doi.org/10.5539/ijef.v4n3p216
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This work was supported by a grant from the Ministry of Research, Innovation and Digitization, CNCS- UEFISCDI, project number PN-III-P4-PCE-2021-0334, within PNCDI III.
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Oprea, SV., Georgescu, I.A. & Bâra, A. Is Bitcoin ready to be a widespread payment method? Using price volatility and setting strategies for merchants. Electron Commer Res (2024). https://doi.org/10.1007/s10660-024-09812-x
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DOI: https://doi.org/10.1007/s10660-024-09812-x