**5. Conclusions**

Using bitcoin and 14 global financial asset price data covering stock, bond, commercial and currency for the period 2013–2021, this study applied the ADCC-GARCH approach to test the dynamic correlation between bitcoin and each asset at different time frequencies, and further identified the risk diversification, hedging and safe-haven properties of bitcoin for those traditional assets. The main findings are as follows:


Our conclusions provide useful insights for market participants and policymakers. First, because bitcoin is closer in nature to a risk asset, investors should allocate to bitcoin as a risk diversifier for traditional risk assets such as stock, bond and commodity, rather than as a hedge, especially in times of extreme exogenous shocks. Second, the short-term high volatility and speculative nature of the bitcoin market leads to great uncertainty in the shortterm price of bitcoin, while also undermining its short-term correlation with major financial assets, making bitcoin's diversification, hedging and safe-haven properties vary across different time-frequency dimensions. This reminds bitcoin holders that it is important to distinguish between bitcoin holding periods. Investors who enter the bitcoin market should opt for long-term holdings as much as possible. Short-term speculation could expose them to significant investment risk and would likely result in large capital losses. Third, as uncertainties in global financial markets further increase in the post-epidemic era, policymakers and investors should keep paying attention to potential structural changes in the linkage between bitcoin and major asset prices under exogenous extreme shocks and the financial risks they may trigger. Finally, as the market for bitcoin trading is immature and the price is extremely unstable, individual investors should be discouraged from entering the cryptocurrency market represented by bitcoin, to protect the safety of their property.

**Author Contributions:** Conceptualization, P.W.; methodology, P.W., X.L. and S.W.; software, P.W., X.L. and S.W.; validation, P.W., X.L. and S.W.; formal analysis, P.W., X.L. and S.W.; investigation, P.W.; resources, P.W., X.L. and S.W.; data curation, P.W., X.L. and S.W.; writing—original draft preparation, P.W.; writing—review and editing, P.W., X.L. and S.W.; visualization, P.W., X.L. and S.W.; supervision, P.W., X.L. and S.W.; project administration, P.W. and X.L.; funding acquisition, P.W. and X.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Zhejiang Provincial Philosophy and Social Science Planning Project of China (grant number 23NDJC023Z); the National Key Research and Development Program of China (grant number 2021QY2100); the National Natural Science Foundation of China (grant number 72173018); and the Zhejiang Provincial Natural Science Foundation of China (grant number LQ21G030005).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data are available on request.

**Acknowledgments:** We would like to thank two anonymous referees and the subject editor of this journal for helpful comments and suggestions on earlier versions of this paper.
