BITCOIN PRICE PREDICTION BY BOX-JENKINS MODEL
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Abstract
Bitcoin (BTC) and other cryptocurrencies have seen an explosion in popular notoriety. Indeed, the price of Bitcoin is known to vary widely. Meanwhile, as Bitcoin's use cases grow, mature, and grow, hype and controversy have swirled. As with any design or commodity on the market, bitcoin trading and financial instruments have quickly followed the public adoption of bitcoin and continue to grow. We will carry out a detailed analysis of Bitcoin prices using time series of the Box-Jenkins model, in particular the closing price. Which Traders will then use.
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References
Box, G.E.P. and Jenkins, G.M. (1976); “ Time Series Analysis, Forecasting and Control” , Holden-Day, San Francisco.
Brito, Jerry. (2014). “Bitcoin: Examining the Benefits and Risks for Small Business,” Statement from Jerry Brito.
Bitcoin Charts. (2020). Bitcoin charts. Available online: https://bitcoincharts.com/charts
Bollerslev, T. 1986. « Generalized autoregressive conditional heteroskedasticity ». Journal of Econometrics, 31, 307-327
Bollerslev, T., Wooldridge, J., 1992. « Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances. » Econ. Rev. 11, 143-172
Bollerslev, T., Engle, R.F., Nelson, D.B., 1994. « ARCH models. In Engle, R.F., McFadden, D. (Eds.) », Handbook of Econometrics, Amsterdam Elsevier Science, pp. 2959-3038.
Brandvold, M., Moln, P., Vagstad, K., and Valstad, O. C. A. 2015. » Price discovery on Bitcoin exchanges ». Journal of International Financial Markets, Institutions and Money 36: 18- 35.
Bouoiyour, J., Selmi, R., Tiwari, A.K. and Olayeni, O.R. 2016. » What drives Bitcoin price? ». Economics Bulletin 36(2): 843-850.
Campbell, John Young, Andrew Wen-Chuan Lo, and Craig MacKinlay. (1996). The Econometrics of Financial Markets.
Catania, Leopoldo, Stefano Grassi, and Francesco Ravazzolo. (2019). Forecasting cryptocurrencies under model and parameter instability. International Journal of Forecasting 35: 485–501.
Koray, Faik, and William Lastrapes. (1989). Real Exchange Rate Volatility and U.S. Bilateral Trade: A Var Approach. The Review of Economics and Statistics 71: 708.
Diewert, W. E. (1998); “Index Number Issues in the Consumer Price Index,” Journal of Economics and Perspectives. Vol. 12, N°. 1, pp. 47–58.
Dicky, W. A. & Fuller, D.A. (1979); “Distribution of Estimates for Autoregressive Time Series with a Unit Root,” Journal the American Statistics Association. Vol. 74, pp. 427–431.
Dickey, W. A. and Fuller, , D.A. (1981); “Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root,” Econometrica. Vol. 49, N°. 4, pp. 1057–1072.
Etuk, E.H., Moffat, I.U and Chims, B.E. (2013); “Modelling Monthly Rainfall Data of Port Harcourt, Nigeria by Seasonal Box-Jenkins Methods”. International Journal of Science, Vol. 2 , pp. 60-67.
Faff, R.W. and McKenzie, M.D., (2007), The relationship between implied volatility and autocorrelation, International Journal of Managerial Finance, 3 (2) pp. 191 – 196
Im, K. S., Pesaran, M. H. and Y. Shin, (2003); “Testing for unit roots in heterogeneous panels,” Journal of Economics., vol. 115, no. 1, pp. 53–74,
Mackinnon, J. G. (1994); “Approximate Asymptotic Distribution Functions for Unit-Root and Cointegration Tests.” Journal of Economics Business and Statistics. Vol. 12, N°. 2, pp. 167–176
MTIRAOUI ; A. & HAJ WANNESS ; K. (2020) : ‘‘L’indice Des Prix à La Consommation (IPC) En Tunisie : Méthode De Box-Jenkins’’. Revue d'économie et de statistique appliquée. (The Consumer Price Index (CPI) in Tunisia: Box-Jenkins Method. Journal of Economics and Applied Statistics) Volume 16, Numéro 2, Pages 7-17
Mtiraoui, A. and al. (2020); Islamic Financial Development Between Political Stability and Economic Growth in the MENA Region : Estimate a Model of Simultaneous Equations. Working Paper. doi:10.2139/ssrn.3472879
Mtiraoui, A. and Talbi, N., (2021); Islamic Financial Development between the Volatility of Inflation and the Revival of Economic Growth in the MENA Region. International Journal of Social Science and Human Research. Volume 4, Issue 11, Pages 3063-3074.
Mtiraoui, A. (2024). Interaction between Migration and Economic Growth through Unemployment in the Context of Political Instability in the MENA Region. International Journal of Economics and Financial.Vol.14, Issues (1); pp. 204–215.
Mtiraoui, A. and Dakhli, A., (2023). Corporate characteristics, audit quality and managerial entrenchment during the COVID-19 crisis: Evidence from an emerging country. International Journal of Productivity and Performance Management. Vol.72, N° 4; pp:1182-1200
Mtiraoui, A. and Snoussi, A. (2024). Analysing the Nexus Between Economic Growth, Institutional Dynamics and Environmental Sustainability in the MENA region post-COVID-19. Russian Law Journal. Vol. 12, N° 1; pp. 1195-1205.
Mtiraoui, A. and al. (2024). Economic growth between institutional quality and energy transition: case of MENA countries. Russian Law Journal. Vol. 12, N° 3; pp. 1195-1205.
Mtiraoui, A. and al. (2021). Institutional Quality, Fight Against Corruption, Energy Consumption and Economic Growth in the MENA Region. International Journal of Progressive Sciences and Technologies. Vol. 26 N°. 2, pp.77-88.
Mtiraoui, A; (2015): Control of corruption, Action of public power, Human capital and Economic development: Application two sectors of education and health in the MENA region. https://mpra.ub.uni-muenchen.de/65004/.’’
Mtiraoui, A. and al. (2019): Islamic Financial Development between Policy Stability and Economic Growth in the MENA region: Estimate a Model of Simultaneous Equations’. SSRN Electronic Journal.
Islamic financial development, fdi and economic growth in MENA and east asia and the pacific: theoretical analysis and empirical study. Russian Law Journal. Vol. 12, N° 3; pp. 1185-1190.
Slimene, N. (2020). Les déterminants de la performance éthique des banques islamiques. Revue d'économie financière. 2020/2 N° 138. Pages 301 à 316.
Olufunke G. Darley,*, Abayomi I. O. Yussuff, Adetokunbo A. Adenowo (2021); “Price Analysis and Forecasting for Bitcoin Using Auto Regressive Integrated Moving Average Model”, Annals of Science and Technology 2021 Vol. 6(2) 47-56
Oyetunji, O. B. (1985); “Inverse Autocorrelations and Moving Average Time Series Modelling” . Journal of Official Statistics, 1, pp. 315 – 322.
Phillips, P. C. and Perron, P. (1988); “Testing for a unit root in time series regression.,” Biometrika. Vol. 75, N°. 2, pp. 335–346,
Perron, P. (1990); “Testing for a Unit Root in a Time Series with a Changing Mean”. Journal Economics Business Statistics. Vol. 8, N°. 2, pp. 153–162.
Romero-Ávila D. and Usabiaga, C. (2009); “The hypothesis of a unit root in OECD inflation revisited,” Journal of Economics Business. Vol. 61, N°2, pp. 153–161.
Saikkonen, P. and Lütkepohl, H. (2002); “Testing for a Unit Root in a Time Series With a Level Shift At Unknown Time,” Economics Theory. Vol. 18, N°. 02.
Taneja, K., Ahmad, S., Ahmad, K. and Attri, S. D. (2016); “Time series analysis of aerosol optical depth over New Delhi using BoxeJenkins ARIMA modeling approach”. Atmospheric Pollution Research, Vol. 7, pp. 585-596.
Abu Bakar, N. and Rosbi. S,(2017) Robust Pearson Correlation Analysis of Volatility for the Islamic
Simon Stevenson, (2007), A comparison of the forecasting ability of ARIMA models, Journal of Property Investment & Finance, 25 (3), pp.223-240