DETERMINANTS OF BITCOIN PRICE MOVEMENTS

Year 2024, , 75 - 78, 30.07.2024

Dilek Lebleci Teker , Suat Teker , Esin Demirel Gümüştepe

https://doi.org/10.17261/Pressacademia.2024.1911

Abstract

Purpose- Investors want to include Bitcoin in their portfolios due to its high returns. However, high returns also come with high risks. For this reason, the volatility prediction of Bitcoin prices is the focus of attention of investors. Because Bitcoin's volatility is used as an important input in portfolio selection and risk management. This means that the models to be used in predicting Bitcoin volatility increases the importance of performance. In this research; A comparative examination of the models applied for Bitcoin shows an effective performance in volatility prediction. It is very important for evaluation. The aim of this study is to model Bitcoin price returns and to examine future return predictions and return directions using historical Bitcoin prices.
Methodology- Many models have been used in studies on financial instruments and price predictions. Models such as linear and nonlinear regression, Random Walk Model, GARCH and ARIMA fall into this category. Nonlinear econometric models such as ARCH and GARCH are used for financial time series with variable volatility. These models assume that the variance is not constant. In this study, first Bitcoin price returns for the period between January 2020 and December 2023 will be modeled with the GARCH model, and then the ARCH-GARCH models will be used for future prediction of returns for the period between January 2024 and June 2024. Finally, the actual values will be compared with the forecasted values. In other words, the primary aim of this study is to use the daily Bitcoin closing price between May 2020 and December 2023 to estimate the returns for the periods of 2024 and compare it with the actual returns.
Findings- The analysis reveals that GARCH Model results showed that in the mean and variance equations, it is seen that all variables are except intercept of the mean equation significant according to the error level of 0.05. Namely, the reaction and persistence parameters are significant accourding to 0.05 in the variance equation. Both the coefficient of the reaction parameter and the coefficient of the persistent parameter are higher than zero (positive). Also, the coefficient of the reaction parameter plus the coefficient of the persistent parameter approximately equals 0.72. That is, it is lower than 1 and higher than zero (positive). The level of persistence is not too high. So, we do not think about non-stationary variance in the model. Reaction parameter’s coefficient is 0.13. And persistence parameter’s coefficient is 0.58. As we can see, persistent parameter is much higher than reaction parameter. That is, when there is a new shock that creates the persistent parameter, that shock will be in effect for a long time, it will not disappear immediately. That is, a significant part of the shock that occurs in one period flows into the next period. After determining the appropriate mean and variance models, a forecast is made using Automatic ARIMA forecasting for BITCOIN return forecasting. This forecast is made for the first five months of 2024, without adding the actual values of the first five months of 2024 to the data. The program ranks the most appropriate model. The program chose GARCH(3,3) as the most appropriate model in "bitcoin return prediction".
Conclusion- The results of the test applied in the study can be summarized that the unit root test results showed that it was necessary to work with return series. GARCH(1,1) model results show when there is a new shock that creates the persistent parameter, that shock will be in effect for a long time, it will not disappear immediately. That is, a significant part of the shock that occurs in one period flows into the next period. According to GARCH automatic forecasting results, the best GARCH model that models Bitcoin return is the GARCH(3,3) model. According to these model results, although the slopes of the actual and forecasted return series move in the same direction, the model remains weak for forecasting. In future studies, it may be recommended to estimate Bitcoin returns with non-linear models.

Keywords

Bitcoin, ARCH models, GARCH models, forecasting, ARIMA models

References

• Almansour, B. Y., Alshater, M. M., & Almansour, A. Y. (2021). Performance of ARCH and GARCH models in forecasting cryptocurrency market volatility. Industrial Engineering & Management Systems, 20(2), 130-139.
• Ardia, D., Bluteau, K., & Rüede, M. (2019). Regime changes in Bitcoin GARCH volatility dynamics. Finance Research Letters, 29, 266-271.
• Bauwens, L., Laurent, S., & Rombouts, J. V. (2006). Multivariate GARCH models: a survey. Journal of applied econometrics, 21(1), 79-109.
• Bhowmik, R., & Wang, S. (2020). Stock market volatility and return analysis: A systematic literature review. Entropy, 22(5), 522-539.
• Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327.
• Caporale, G. M., & Zekokh, T. (2019). Modelling volatility of cryptocurrencies using Markov-Switching GARCH models. Research in International Business and Finance, 48, 143-155.
• Chu, J., Chan, S., Nadarajah, S., & Osterrieder, J. (2017). GARCH modelling of cryptocurrencies. Journal of Risk and Financial Management, 10(4), 17-31.
• Conrad, C., Custovic, A., & Ghysels, E. (2018). Long-and short-term cryptocurrency volatility components: A GARCH-MIDAS analysis. Journal of Risk and Financial Management, 11(2), 23-34.
• Engle, R. (2001). ARCH/GARCH Models in Applied. The Journal of Economic Perspectives, 15(4), 157-168.
• Engel C, Frankel JA, Froot KA, Rodrigues AP. 1995. Tests of conditional mean-variance efficiency of the U.S. stock market. Journal of Empirical Finance, 2(1), 3–18.
• Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the econometric society, 987-1007.
• Engle, R. F. (1983). Estimates of the Variance of US Inflation Based upon the ARCH Model. Journal of money, credit and banking, 15(3), 286-301.
• Fauzi, M. A., Paiman, N., & Othman, Z. (2020). Bitcoin and cryptocurrency: Challenges, opportunities and future works. The Journal of Asian Finance, Economics and Business, 7(8), 695-704.
• Gunawan, D., & Febrianti, I. (2023). Ethereum Value Forecasting Model using Autoregressive Integrated Moving Average (ARIMA). International Journal of Advances in Social Sciences and Humanities, 2(1), 29-35.
• Gyamerah, S. A. (2019). Modelling the volatility of Bitcoin returns using GARCH models. Quantitative Finance and Economics, 3(4), 739-753.
• Katsiampa, P. (2017). Volatility estimation for Bitcoin: A comparison of GARCH models. Economics Letters, 158, 3-6.
• Kyriazis, Ν. A., Daskalou, K., Arampatzis, M., Prassa, P., & Papaioannou, E. (2019). Estimating the volatility of cryptocurrencies during bearish markets by employing GARCH models. Heliyon, 5(8), 1-8.
• Lundbergh, S., & Teräsvirta, T. (2002). Evaluating GARCH models. Journal of Econometrics, 110(2), 417-435.
• Luther, W. J. (2016). Bitcoin and the future of digital payments. The Independent Review, 20(3), 397-404.
• McKinnon, R. I. (1991). Financial control in the transition from classical socialism to a market economy. Journal of Economic Perspectives, 5(4), 107-122.
• Naimy, V. Y., & Hayek, M. R. (2018). Modelling and predicting the Bitcoin volatility using GARCH models. International Journal of Mathematical Modelling and Numerical Optimisation, 8(3), 197-215.
• Pallathadka, H., Tongkachok, K., Arbune, P. S., & Ray, S. (2022). Cryptocurrency and Bitcoin: Future Works, Opportunities, and Challenges. ECS Transactions, 107(1), 16313.
• Quan, Y. X., Yang, T. X., Fei, C. Y., Cheong, C. W., & Min, L. (2023). Asymmetric Volatility and Risk Analysis of Bitcoin Cryptocurrency Market. Journal of Quality Measurement and Analysis JQMA, 19(2), 73-79.
• Queiroz, R. G., & David, S. A. (2023). Performance of the Realized-GARCH Model against Other GARCH Types in Predicting Cryptocurrency Volatility. Risks, 11(12), 211-223.
• Rahman, A., & Dawood, A. K. (2019). Bitcoin and future of cryptocurrency. Ushus Journal of Business Management, 18(1), 61-66.
• Yıldırım, H., & Bekun, F. V. (2023). Predicting volatility of bitcoin returns with ARCH, GARCH and EGARCH models. Future Business Journal, 9(1), 75-82.