The impact of economic uncertainty on the stock market and cryptocurrency market

Policy uncertainty and geopolitical issues have become increasingly severe recently, including the China-U.S. trade friction in 2018, the Russia-Ukraine war in 2022, and the Hamas-Israel conflict in 2023. A series of unstable events has caused turbulence in the financial system, causing investors and financial institutions to look for ways to diversify risks and reduce losses. This thesis aims to explore the relationship between stock markets, assets that function as hedges or safe havens, and the factors that influence the prices of these assets under uncertain political and economic events. Therefore, firstly, this thesis uses the ARCH model to determine that both the Chinese and U.S. stock markets have heteroskedasticity before and during the trade friction period, and the DCC-GARCH model further determined the co-movement of the Chinese and U.S. stock markets before and after the trade friction period. Secondly, this thesis uses TVP-VAR and SHAP models to explore the short-term time-varying asymmetric volatility spillover effects between cryptocurrencies and the Chinese and U.S. stock markets. Then, I use DECO and CDB models to further compare the returns between the cryptocurrencies and mix (cryptocurrencies and stock indices) investment portfolios. This thesis finds that a mixed investment portfolio has higher returns and diversity of risk during the uncertainty period. Finally, the traditional time series model VAR and the machine learning-based BPNN detect the impact of the number of active addresses of BTC, ETH and USDT, as well as CFGI index on the BTC price. Among them, investors prefer USDT as a stable currency during economic instability. This thesis offers innovative portfolio management strategies for investors and financial institutions during economic instability. The findings indicate that employing cryptocurrencies as short‐term hedges or incorporating them as safe haven assets can diversify risk and enhance returns.