Determinants of Bitcoin price movements

dc.authorid0000-0002-3893-4015
dc.authorid0000-0002-7981-3121
dc.authorid0000-0003-4257-6780
dc.contributor.authorTeker, Dileken_US
dc.contributor.authorTeker, Suaten_US
dc.contributor.authorDemirel, Esinen_US
dc.date.accessioned2025-07-07T12:28:00Z
dc.date.available2025-07-07T12:28:00Z
dc.date.issued2024-07-30
dc.departmentIşık Üniversitesi, İktisadi, İdari ve Sosyal Bilimler Fakültesi, İşletme Bölümüen_US
dc.departmentIşık University, Faculty of Economics, Administrative and Social Sciences, Department of Managementen_US
dc.description.abstractPurpose- 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.en_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationLebleci Teker, D., Teker, S. & Demirel Gümüştepe, E. (2024). Determinants of Bitcoin price movements. PressAcademia Procedia, 19(1), 75-78. doi:https://doi.org/10.17261/Pressacademia.2024.1911en_US
dc.identifier.doi10.17261/Pressacademia.2024.1911
dc.identifier.endpage78
dc.identifier.issn2459-0762
dc.identifier.issue1
dc.identifier.startpage75
dc.identifier.urihttps://hdl.handle.net/11729/6530
dc.identifier.urihttps://doi.org/10.17261/Pressacademia.2024.1911
dc.identifier.volume19
dc.indekslendigikaynakSobiaden_US
dc.institutionauthorTeker, Dileken_US
dc.institutionauthorTeker, Suaten_US
dc.institutionauthorDemirel, Esinen_US
dc.institutionauthorid0000-0002-3893-4015
dc.institutionauthorid0000-0002-7981-3121
dc.institutionauthorid0000-0003-4257-6780
dc.language.isoen
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherSuat Tekeren_US
dc.relation.ispartofPressAcademia Procediaen_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectBitcoinen_US
dc.subjectARCH modelsen_US
dc.subjectGARCH modelsen_US
dc.subjectForecastingen_US
dc.subjectARIMA modelsen_US
dc.titleDeterminants of Bitcoin price movementsen_US
dc.typeArticleen_US
dspace.entity.typePublicationen_US

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