本論文想藉由因子模型探討加密貨幣的超額報酬和金融壓力及經濟政策不確定性之關聯，使用Deng et al. (2019) 的四因子模型，其中包含市場因子、規模因子、流動性因子及動能因子，並再加入金融壓力指數 (FSI) 及經濟政策不確定性指數 (EPU)，並採用不同國家的EPU加以分析其結果是否存在差異。實證結果顯示，加密貨幣超額報酬和金融壓力指數不存在顯著相關性，但兩者關係趨向負相關，另外和全球EPU指數則部分呈現負向顯著相關，即當經濟政策的不確定性增加時，加密貨幣的超額報酬會下降，但不受單一國家EPU影響。

This thesis uses the factor model to explore the relationship between cryptocurrency’s excess return and financial stress and economic policy uncertainty. Using the four-factor model of Deng et al. (2019), which includes market, size, liquidity and contrarian factors, we further add the Financial Stress Index (FSI) and Economic Policy Uncertainty Index (EPU) into the factor model, and also use the EPU of different countries to analyze whether the results are different. The empirical results show that there is no significant correlation between cryptocurrency’s excess return and financial stress index, but the relationship between the two tends to be negatively correlated. In addition, it is partially negatively correlated with the global EPU index, that is, when the uncertainty of economic policy increases, the return of the cryptocurrency will decrease, but it is not affected by the EPU of a single country.

Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. Quarterly Journal of Economics, 131(4), 1593-1636.
Bariviera, A. F. (2017). The inefficiency of Bitcoin revisited: A dynamic approach. Economics Letters, 161, 1-4.
Bouri, E., Gupta, R., Lau, C. K. M., Roubaud, D., & Wang, S. (2018). Bitcoin and global financial stress: A copula-based approach to dependence and causality in the quantiles. The Quarterly Review of Economics and Finance, 69, 297-307.
Bouri, E., Molnár, P., Azzi, G., Roubaud, D., & Hagfors, L. I. (2017). On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier?. Finance Research Letters, 20, 192-198.
Brogaard, J., & Detzel, A. (2015). The asset-pricing implications of government economic policy uncertainty. Management Science, 61(1), 3-18.
Cheah, E. T., & Fry, J. (2015). Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin. Economics Letters, 130, 32-36.
Ciaian, P., Rajcaniova, M., & Kancs, D. A. (2016). The economics of BitCoin price formation. Applied Economics, 48(19), 1799-1815.
Corbet, S., Lucey, B., Urquhart, A., & Yarovaya, L. (2019). Cryptocurrencies as a financial asset: A systematic analysis. International Review of Financial Analysis, 62, 182-199.
Corbet, S., Meegan, A., Larkin, C., Lucey, B., & Yarovaya, L. (2018). Exploring the dynamic relationships between cryptocurrencies and other financial assets. Economics Letters, 165, 28-34.
Davis, S. J. (2016). An index of global economic policy uncertainty (No. w22740). National Bureau of Economic Research.
Deng, X., Chuen, D. L. K., Wang, Y., & Zhang, X. (2019). A Five-Factor Cryptocurrency Pricing Model. Working paper.
Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica: journal of the Econometric Society, 1057-1072.
Dyhrberg, A. H. (2016). Bitcoin, gold and the dollar–A GARCH volatility analysis. Finance Research Letters, 16, 85-92.
Dyhrberg, A. H. (2016). Hedging capabilities of bitcoin. Is it the virtual gold?. Finance Research Letters, 16, 139-144.
Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3-56.
Georgoula, I., Pournarakis, D., Bilanakos, C., Sotiropoulos, D., & Giaglis, G. M. (2015). Using time-series and sentiment analysis to detect the determinants of bitcoin prices. Available at SSRN 2607167.
Hu, A. S., Parlour, C. A., & Rajan, U. (2019). Cryptocurrencies: Stylized facts on a new investible instrument. Financial Management, 48(4), 1049-1068.
Jo, H., Park, H., & Shefrin, H. (2018). Bitcoin and Sentiment. Working paper.
Phillips, P. C., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335-346.
Stenqvist, E., & Lönnö, J. (2017). Predicting Bitcoin price fluctuation with Twitter sentiment analysis. Working paper.
Wang, G. J., Xie, C., Wen, D., & Zhao, L. (2019). When Bitcoin meets economic policy uncertainty (EPU): Measuring risk spillover effect from EPU to Bitcoin. Finance Research Letters, 31.
Federal Reserve Bank of St. Louis, St. Louis Fed Financial Stress Index [STLFSI], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/STLFSI.
Paul Vigna and Michael J. Casey (2017)。《虛擬貨幣革命》。林奕伶譯。新北市。大牌出版公司。