Major determinants of Bitcoin price: Application of a vector error correction model

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Research in recent years has shown that Bitcoin is a virtual asset that is used as a medium of exchange and investment tool other than shares and bonds, the development of the digital era has opened up opportunities for Bitcoin to be chosen as part of an investor’s portfolio. The focus of this study is to examine the impact of nine key determinants on Bitcoin price. The data used in the study are daily data starting from January 1, 2018 to January 1, 2022. The main data source is taken from Investing.com, and the estimation method applied is the Vector Error Correction Model (VECM). The main finding shows that Bitcoin Volume impacts Bitcoin Price negatively, which is in line with the demand theory. Another finding is related to the substitute effect of Ethereum Volume, Litecoin Volume, and Gold Volume, each of which influences Bitcoin Price positively, suggesting that these three commodities are substitutes to Bitcoin. In contrast, whereas Oil Volume has an insignificant effect on Bitcoin price in the short term, it has a negative significant impact in the long term. In addition, LQ45 stock index Volume influences Bitcoin Price positively in the short term, suggesting that LQ45 stock index and Bitcoin substitute for each other. Moreover, Google Trends impacts Bitcoin price positively in the long term. In terms of the income effect, either the Indonesian GDP or US GDP has a strong positive effect on Bitcoin price in both the short and long term.

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    • Figure 1. AR roots graph
    • Figure 2. Impulse response function graph
    • Table 1. Descriptive statistics of the selected variables
    • Table 2. Unit root test (1st difference)
    • Table 3. Lag length criteria
    • Table 4. Johansen’s co-integration test
    • Table 5. Error correction terms
    • Table 6. Estimation results of the short-run (Vector Error Correction Model)
    • Table 7. Estimation results of the long-run relationship (Ordinary Least Squared)
    • Table 8. Variance decomposition
    • Table 9. Summary of hypothesis testing
    • Conceptualization
      Dermawan Jaya Hartono, Suyanto Suyanto
    • Data curation
      Dermawan Jaya Hartono
    • Formal Analysis
      Dermawan Jaya Hartono, Suyanto Suyanto
    • Investigation
      Dermawan Jaya Hartono
    • Methodology
      Dermawan Jaya Hartono, Suyanto Suyanto
    • Software
      Dermawan Jaya Hartono
    • Visualization
      Dermawan Jaya Hartono
    • Writing – original draft
      Dermawan Jaya Hartono, Suyanto Suyanto
    • Writing – review & editing
      Dermawan Jaya Hartono, Suyanto Suyanto
    • Funding acquisition
      Suyanto Suyanto
    • Project administration
      Suyanto Suyanto
    • Resources
      Suyanto Suyanto
    • Supervision
      Suyanto Suyanto
    • Validation
      Suyanto Suyanto