Estimating and forecasting bitcoin daily prices using ARIMA-GARCH models
Purpose – The goal of the study is to offer important insights into the dynamics of the cryptocurrency market by analyzing pricing data for Bitcoin. Using quantitative analytic methods, the study makes use of a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and an Autoregres...
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| Format: | Article |
| Language: | English |
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Emerald Publishing
2024-10-01
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| Series: | Business Analyst |
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| Online Access: | https://www.emerald.com/insight/content/doi/10.1108/BAJ-05-2024-0027/full/pdf |
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| author | Quang Phung Duy Oanh Nguyen Thi Phuong Hao Le Thi Hai Duong Pham Hoang Khanh Linh Luong Kim Ngan Nguyen Thi |
| author_facet | Quang Phung Duy Oanh Nguyen Thi Phuong Hao Le Thi Hai Duong Pham Hoang Khanh Linh Luong Kim Ngan Nguyen Thi |
| author_sort | Quang Phung Duy |
| collection | DOAJ |
| description | Purpose – The goal of the study is to offer important insights into the dynamics of the cryptocurrency market by analyzing pricing data for Bitcoin. Using quantitative analytic methods, the study makes use of a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and an Autoregressive Integrated Moving Average (ARIMA). The study looks at how predictable Bitcoin price swings and market volatility will be between 2021 and 2023. Design/methodology/approach – The data used in this study are the daily closing prices of Bitcoin from Jan 17th, 2021 to Dec 17th, 2023, which corresponds to a total of 1065 observations. The estimation process is run using 3 years of data (2021–2023), while the remaining (Jan 1st 2024 to Jan 17th 2024) is used for forecasting. The ARIMA-GARCH method is a robust framework for forecasting time series data with non-seasonal components. The model was selected based on the Akaike Information Criteria corrected (AICc) minimum values and maximum log-likelihood. Model adequacy was checked using plots of residuals and the Ljung–Box test. Findings – Using the Box–Jenkins method, various AR and MA lags were tested to determine the most optimal lags. ARIMA (12,1,12) is the most appropriate model obtained from the various models using AIC. As financial time series, such as Bitcoin returns, can be volatile, an attempt is made to model this volatility using GARCH (1,1). Originality/value – The study used partially processed secondary data to fit for time series analysis using the ARIMA (12,1,12)-GARCH(1,1) model and hence reliable and conclusive results. |
| format | Article |
| id | doaj-art-f4d2a4d92fc04f9aa21b4e1d9d3d838c |
| institution | Kabale University |
| issn | 0973-211X 2754-6721 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Emerald Publishing |
| record_format | Article |
| series | Business Analyst |
| spelling | doaj-art-f4d2a4d92fc04f9aa21b4e1d9d3d838c2025-02-03T14:29:23ZengEmerald PublishingBusiness Analyst0973-211X2754-67212024-10-01451112310.1108/BAJ-05-2024-0027Estimating and forecasting bitcoin daily prices using ARIMA-GARCH modelsQuang Phung Duy0Oanh Nguyen Thi1Phuong Hao Le Thi2Hai Duong Pham Hoang3Khanh Linh Luong4Kim Ngan Nguyen Thi5Faculty of Technology and Data Science, Foreign Trade University, Hanoi, VietnamSchool of Economics and International Business, Foreign Trade University, Hanoi, VietnamSchool of Economics and International Business, Foreign Trade University, Hanoi, VietnamSchool of Economics and International Business, Foreign Trade University, Hanoi, VietnamSchool of Economics and International Business, Foreign Trade University, Hanoi, VietnamSchool of Economics and International Business, Foreign Trade University, Hanoi, VietnamPurpose – The goal of the study is to offer important insights into the dynamics of the cryptocurrency market by analyzing pricing data for Bitcoin. Using quantitative analytic methods, the study makes use of a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and an Autoregressive Integrated Moving Average (ARIMA). The study looks at how predictable Bitcoin price swings and market volatility will be between 2021 and 2023. Design/methodology/approach – The data used in this study are the daily closing prices of Bitcoin from Jan 17th, 2021 to Dec 17th, 2023, which corresponds to a total of 1065 observations. The estimation process is run using 3 years of data (2021–2023), while the remaining (Jan 1st 2024 to Jan 17th 2024) is used for forecasting. The ARIMA-GARCH method is a robust framework for forecasting time series data with non-seasonal components. The model was selected based on the Akaike Information Criteria corrected (AICc) minimum values and maximum log-likelihood. Model adequacy was checked using plots of residuals and the Ljung–Box test. Findings – Using the Box–Jenkins method, various AR and MA lags were tested to determine the most optimal lags. ARIMA (12,1,12) is the most appropriate model obtained from the various models using AIC. As financial time series, such as Bitcoin returns, can be volatile, an attempt is made to model this volatility using GARCH (1,1). Originality/value – The study used partially processed secondary data to fit for time series analysis using the ARIMA (12,1,12)-GARCH(1,1) model and hence reliable and conclusive results.https://www.emerald.com/insight/content/doi/10.1108/BAJ-05-2024-0027/full/pdfVolatilityForecastingBitcoinARIMAGARCH |
| spellingShingle | Quang Phung Duy Oanh Nguyen Thi Phuong Hao Le Thi Hai Duong Pham Hoang Khanh Linh Luong Kim Ngan Nguyen Thi Estimating and forecasting bitcoin daily prices using ARIMA-GARCH models Business Analyst Volatility Forecasting Bitcoin ARIMA GARCH |
| title | Estimating and forecasting bitcoin daily prices using ARIMA-GARCH models |
| title_full | Estimating and forecasting bitcoin daily prices using ARIMA-GARCH models |
| title_fullStr | Estimating and forecasting bitcoin daily prices using ARIMA-GARCH models |
| title_full_unstemmed | Estimating and forecasting bitcoin daily prices using ARIMA-GARCH models |
| title_short | Estimating and forecasting bitcoin daily prices using ARIMA-GARCH models |
| title_sort | estimating and forecasting bitcoin daily prices using arima garch models |
| topic | Volatility Forecasting Bitcoin ARIMA GARCH |
| url | https://www.emerald.com/insight/content/doi/10.1108/BAJ-05-2024-0027/full/pdf |
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