Testing bitcoin’s safe-haven property and the correlation between Bitcoin, gold, oil, stock markets, and Google trends

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Since its public introduction in 2009, Bitcoin has grown to be the most well-known cryptocurrency worldwide. There is still debate as to whether Bitcoin may be used as a hedge against other assets. The purpose of this study is to investigate the correlation between Bitcoin and conventional commodity markets such as gold, crude oil, stock markets, and investor interest (quantified via Google Trends). In addition, the paper also tests Bitcoin’s safe haven role compared to other commodity markets. The Vector Autoregression model using daily database collected during the period 2013–2021 is employed to investigate the relationship between Bitcoin and traditional commodity markets. The impulse response function is used to analyze Bitcoin price movements against economic shocks from gold, oil prices, and the Dow Jones Industrial Average. In addition, the value-at-risk (VaR) model is used to test Bitcoin’s safe-haven property compared to other conventional commodity markets. The research results show that Bitcoin has negative impacts on gold, crude oil prices, and the stock market. Besides, Bitcoin responds negatively to a sharp decline in investor interest. Furthermore, the results of the VaR model show that Bitcoin is the second most volatile and risky asset, only after the crude oil market, and much riskier than gold. This result proves that Bitcoin cannot yet be considered a safe-haven instrument. These findings have several implications for investors and policymakers to minimize the risks associated with this cryptocurrency.

Acknowledgment
The authors would like to send their sincere thanks to the Reviewers and Editorial Board of the Journal. Their valuable comments and helpful support helped improve the paper’s quality. No funding was granted for this study.

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    • Figure 1. Response to structural VAR innovations
    • Table 1. Descriptive statistics of variables
    • Table 2. Results of testing the stationarity of the data series
    • Table 3. Testing Granger causality
    • Table 4. Volatility and VaR for the 1% and 5% during the period 2013–2021
    • Conceptualization
      Lien Thi Huong Nguyen, Hanh Hong Vu, Anh Phuong Le
    • Data curation
      Lien Thi Huong Nguyen, Hanh Hong Vu, Anh Phuong Le
    • Formal Analysis
      Lien Thi Huong Nguyen, Hanh Hong Vu, Anh Phuong Le
    • Investigation
      Lien Thi Huong Nguyen, Hanh Hong Vu, Anh Phuong Le
    • Methodology
      Lien Thi Huong Nguyen, Hanh Hong Vu, Anh Phuong Le
    • Project administration
      Lien Thi Huong Nguyen, Hanh Hong Vu
    • Resources
      Lien Thi Huong Nguyen
    • Software
      Lien Thi Huong Nguyen, Hanh Hong Vu, Anh Phuong Le
    • Supervision
      Lien Thi Huong Nguyen
    • Validation
      Lien Thi Huong Nguyen, Hanh Hong Vu
    • Visualization
      Lien Thi Huong Nguyen, Hanh Hong Vu, Anh Phuong Le
    • Writing – original draft
      Lien Thi Huong Nguyen, Hanh Hong Vu, Anh Phuong Le
    • Writing – review & editing
      Lien Thi Huong Nguyen