Predicting volatility of bitcoin returns with ARCH, GARCH and EGARCH models
Abstract The ınvestment decisions of institutional and individual investors in financial markets are largely influenced by market uncertainty and volatility of the investment instruments. Thus, the prediction of the uncertainty and volatilities of the prices and returns of the investment instruments...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-09-01
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| Series: | Future Business Journal |
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| Online Access: | https://doi.org/10.1186/s43093-023-00255-8 |
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| author | Hakan Yıldırım Festus Victor Bekun |
| author_facet | Hakan Yıldırım Festus Victor Bekun |
| author_sort | Hakan Yıldırım |
| collection | DOAJ |
| description | Abstract The ınvestment decisions of institutional and individual investors in financial markets are largely influenced by market uncertainty and volatility of the investment instruments. Thus, the prediction of the uncertainty and volatilities of the prices and returns of the investment instruments becomes imperative for successful investment. In this study we seek to identify the best fit model that can predict the volatility of return of Bitcoin, which is in high demand as an investment tool in recent times. Using the opening data of weekly Bitcoin prices for the period of 11.24.2013–03.22.2020, their logarithmic returns were calculated. The stationarity properties of the Bitcoin return series was tested by applying the ADF unit root test and the series were found to be stationary. After reaching the average equation model as ARMA (2.2), it was tested whether there was an ARCH effect in the ARMA (2,2) model. As a result of the applied ARCH-LM test, it is reached that the residuals of the average equation model selected have ARCH effect. Volatility of Bitcoin return series after detection of ARCH effect has been tried to predict with conditional variance models such as ARCH (1), ARCH (2), ARCH (3), GARCH (1,1), GARCH (1,2), GARCH (1,3), GARCH (2,1), GARCH (2,2), EGARCH (1,1) and EGARCH (1,2). While the obtained findings indicate that the best model is in the direction of GARCH (1,1) according to Akaike info criterion, it was found that GARCH (1,1) model does not have ARCH effect as a result of the applied ARCH-LM test. Thus, our empirical findings highlight an ample guide on appropriate modeling of price information in the Bitcoin market. |
| first_indexed | 2024-03-09T15:09:51Z |
| format | Article |
| id | doaj.art-7272c725b0b24db2909d67d30dea4866 |
| institution | Directory Open Access Journal |
| issn | 2314-7210 |
| language | English |
| last_indexed | 2025-03-14T03:24:44Z |
| publishDate | 2023-09-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Future Business Journal |
| spelling | doaj.art-7272c725b0b24db2909d67d30dea48662025-03-09T12:25:36ZengSpringerOpenFuture Business Journal2314-72102023-09-01911810.1186/s43093-023-00255-8Predicting volatility of bitcoin returns with ARCH, GARCH and EGARCH modelsHakan Yıldırım0Festus Victor Bekun1Department of Logistic Management, Faculty of Economic, Administrative and Social Sciences, Istanbul Gelisim UniversityDepartment of Logistic Management, Faculty of Economic, Administrative and Social Sciences, Istanbul Gelisim UniversityAbstract The ınvestment decisions of institutional and individual investors in financial markets are largely influenced by market uncertainty and volatility of the investment instruments. Thus, the prediction of the uncertainty and volatilities of the prices and returns of the investment instruments becomes imperative for successful investment. In this study we seek to identify the best fit model that can predict the volatility of return of Bitcoin, which is in high demand as an investment tool in recent times. Using the opening data of weekly Bitcoin prices for the period of 11.24.2013–03.22.2020, their logarithmic returns were calculated. The stationarity properties of the Bitcoin return series was tested by applying the ADF unit root test and the series were found to be stationary. After reaching the average equation model as ARMA (2.2), it was tested whether there was an ARCH effect in the ARMA (2,2) model. As a result of the applied ARCH-LM test, it is reached that the residuals of the average equation model selected have ARCH effect. Volatility of Bitcoin return series after detection of ARCH effect has been tried to predict with conditional variance models such as ARCH (1), ARCH (2), ARCH (3), GARCH (1,1), GARCH (1,2), GARCH (1,3), GARCH (2,1), GARCH (2,2), EGARCH (1,1) and EGARCH (1,2). While the obtained findings indicate that the best model is in the direction of GARCH (1,1) according to Akaike info criterion, it was found that GARCH (1,1) model does not have ARCH effect as a result of the applied ARCH-LM test. Thus, our empirical findings highlight an ample guide on appropriate modeling of price information in the Bitcoin market.https://doi.org/10.1186/s43093-023-00255-8Bitcoin volumeVolatility returnsARMAARCHGARCH |
| spellingShingle | Hakan Yıldırım Festus Victor Bekun Predicting volatility of bitcoin returns with ARCH, GARCH and EGARCH models Future Business Journal Bitcoin volume Volatility returns ARMA ARCH GARCH |
| title | Predicting volatility of bitcoin returns with ARCH, GARCH and EGARCH models |
| title_full | Predicting volatility of bitcoin returns with ARCH, GARCH and EGARCH models |
| title_fullStr | Predicting volatility of bitcoin returns with ARCH, GARCH and EGARCH models |
| title_full_unstemmed | Predicting volatility of bitcoin returns with ARCH, GARCH and EGARCH models |
| title_short | Predicting volatility of bitcoin returns with ARCH, GARCH and EGARCH models |
| title_sort | predicting volatility of bitcoin returns with arch garch and egarch models |
| topic | Bitcoin volume Volatility returns ARMA ARCH GARCH |
| url | https://doi.org/10.1186/s43093-023-00255-8 |
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