本研究旨在探討2020年初突發的黑天鵝事件-新冠肺炎疫情(COVID-19)前後對全球金融市場及主要商品的影響。並以美元指數、黃金、石油對S&P500指數的外溢效應做為研究對象。
本文利用2019年9月1日至2020年10月31日間在美國期貨交易所發行之S&P500期貨、WTI原油期貨、黃金期貨、美元指數期貨，透過將疫情前後時段作區分，並使用GARCH模型及VECM模型比較美元、石油、黃金等變數對股市變數在此種不確定且不正常的情況下是否有何連動性，並研究是否能有某種關係而使的投資人能提前發現，從而避免因變數的暴漲暴跌而導致巨額虧損的情況發生。 
    本文的實證結果顯示，在黑天鵝事件影響下，WTI原油會對S&P500造成助漲助跌的效果，並且黃金確實在股市大跌時可以做為資金的避風港，而美元指數則對股市的影響不大，但基本上也是對於股市呈現反向連動的關係。因此投資人可在資產配置時，藉由以上變數的關係決定自己的進出場方向。

This study aims to explore the impact of the black swan incident in early 2020-the new crown pneumonia epidemic (COVID-19) on the global financial market and major commodities. And take the spillover effects of the US dollar index, gold, and oil on the S&P500 index as the research objects.
	This article uses S&P500 futures, WTI crude oil futures, gold futures, and U.S. dollar index futures issued on the U.S. Futures Exchange from September 1, 2019 to October 31, 2020. By dividing the time period before and after the epidemic, the GARCH model and VECM are used. The model compares whether variables such as U.S. dollar, oil, gold, etc. have any relevance to stock market variables under such uncertain and abnormal circumstances, and studies whether there is a relationship that enables investors to find out in advance, so as to avoid dependent variables The sudden rise and fall of the country led to huge losses.
	The empirical results of this article show that under the influence of the black swan event, WTI crude oil will have the effect of helping the S&P500 rise and fall, and gold can indeed be used as a safe haven for funds when the stock market plummets, while the US dollar index has no effect on the stock market. Large, but basically showing a reverse linkage relationship with the stock market. Therefore, investors can decide their own entry and exit directions based on the relationship of the above variables during asset allocation.

目　錄
目　錄	iii
表　目　錄	v
圖　目　錄	vi
第一章　緒論	1
第一節 研究背景與動機	1
第二節 研究目的	4
第三節 研究架構與流程	5
第二章　理論與相關文獻	7
第一節 探討黃金價格對股票市場指數之影響的相關文獻	7
第二節 探討石油價格對股票市場指數之影響的相關文獻	8
第三節 探討美元對股票市場指數之影響的相關文獻	10
第四節 探討外溢效應的相關文獻	12
第三章 研究方法	14
第一節 研究資料定義與來源	14
第二節 ADF單根檢定	16
第三節 共整合檢定	17
第四節 向量誤差修正模型(VECM)	18
第五節 ARCH、GARCH模型	19



第四章　實證結果與分析	21
第一節 敘述統計量分析	21
第二節 ADF單根檢定	30
第三節 共整合檢定	32
第四節 向量誤差修正模型(VECM)	34
第五節 GARCH模型	37
第五章　結論與建議	46
第一節 結論	46
第一節 建議	47
參考文獻	48

 
 表　目　錄
表 1變數之合約規格及預期符號表	15
表 2四大變數期貨報酬率之基本敘述統計量(疫情前)	22
表 3四大變數期貨報酬率之基本敘述統計量(疫情後)	26
表 4 四大變數期貨報酬率之ADF單根檢定表(疫情前)	30
表 5 四大變數期貨報酬率之ADF單根檢定表(疫情後)	31
表 6 黃金期貨報酬率之共整合檢定表(疫情前)	32
表 7 石油期貨報酬率之共整合檢定表(疫情前)	32
表 8美元指數期貨報酬率之共整合檢定表(疫情前)	33
表 9 黃金期貨報酬率之共整合檢定表(疫情後)	33
表 10石油期貨報酬率之共整合檢定表(疫情後)	33
表 11美元指數期貨報酬率之共整合檢定表(疫情後)	33
表 12向量誤差修正模型表(疫情前)	35
表 13向量誤差修正模型表(疫情後)	36
表 14黃金、石油及美元指數對S&P500之ARCH Test表(疫情前)	37
表 15黃金、石油及美元指數對S&P500之ARCH Test表(疫情後)	39
表 16黃金、石油及美元指數對S&P500之GARCH(1,1)參數估計結果
(疫情前及疫情後)	41
表 17黃金、石油及美元指數對S&P500之GARCH(1,1)四大指標表
(疫情前及疫情後)	41

 
圖　目　錄
圖 1 研究架構流程圖	6
圖 2 四大變數之箱型圖(疫情前)	22
圖 3 S&P500期貨報酬率時間序列圖(疫情前)	23
圖 4黃金期貨報酬率時間序列圖(疫情前)	23
圖 5 WTI原油期貨報酬率時間序列圖(疫情前)	23
圖 6美元指數期貨報酬率時間序列圖(疫情前)	23
圖 7四大變數之次數分配圖(疫情前)	24
圖 8四大變數之Q-Q圖(疫情前)	25
圖 9四大變數之箱型圖(疫情後)	26
圖 10 S&P500期貨報酬率時間序列圖(疫情後)	27
圖 11黃金期貨報酬率時間序列圖(疫情後)	27
圖 12 WTI原油期貨報酬率時間序列圖(疫情後)	27
圖 13美元指數期貨報酬率時間序列圖(疫情後)	27
圖 14四大變數之次數分配圖(疫情後)	28
圖 15四大變數之Q-Q圖(疫情後)	29
圖 16黃金ARCH Test殘差圖(疫情前)	37
圖 17石油ARCH Test殘差圖(疫情前)	38
圖 18美元指數ARCH Test殘差圖(疫情前)	38
圖 19黃金ARCH Test殘差圖(疫情後)	39
圖 20石油ARCH Test殘差圖(疫情後)	40
圖 21美元指數ARCH Test殘差圖(疫情後)	40
圖 22黃金GARCH(1,1) Test殘差圖(疫情前)	42
圖 23石油GARCH(1,1) Test殘差圖(疫情前)	42
圖 24美元指數GARCH(1,1) Test殘差圖(疫情前)	42
圖 25黃金GARCH(1,1) Test殘差圖(疫情後)	43
圖 26石油GARCH(1,1) Test殘差圖(疫情後)	43
圖 27美元指數GARCH(1,1) Test殘差圖(疫情後)	43

參考文獻
1.	Afsal1, E., and M. I. Haque, (2016), “Market Interactions in Gold and Stock Markets: Evidences from Saudi Arabia,” International Journal of Economics and Financial Issues, Vol. 6, pp.1025-1034.
2.	Arfaoui, M., and A. Rejeb, (2017), “Oil, Gold, US Dollar and Stock Market Interdependencies: A Global Analytical Insight,” European Journal of Management and Business Economics, Vol. 26, pp.278-293.
3.	Caporale, G. M., J. Hunter, and F. M. Ali, (2014), “On the Linkages Between Stock Prices and Exchange Rates: Evidence from The Banking Crisis of 2007–2010,” International Review of Financial Analysis, Vol. 33, pp.87-103.
4.	Christopher, J., and S. Beyer, (2015), “Stock Returns and The U.S. Dollar: The Importance of Monetary Policy,” Managerial Finance, Vol. 41, pp.1046-1058.
5.	Dirk, G., and M. Lucey, (2010), “Is Gold a Hedge or a Safe Haven? An Analysis of Stocks, Bonds and Gold,” The Financial Review, Vol. 45, pp.217-229.
6.	Dirk, G., and K. McDermott, (2010), “Is Gold a Safe Haven? International Evidence,” Journal of Banking and Finance, Vol. 34, pp.1886-1898.
7.	Delgado, N. A., E. B. Delgado, and E. Saucedo, (2018), “The Relationship Between Oil Prices, the Stock Market and the Exchange Rate: Evidence from Mexico,” North American Journal of Economics and Finance, Vol. 45, pp.266-275.
8.	Jorion, P., (1990), “The Exchange Rate Exposure of U.S. Multinationals,” The Journal of Business, Vol. 63, pp.331-345.
9.	Kim, K. H., (2003), “Dollar Exchange Rate and Stock Price: Evidence from Multivariate Cointegration and Error Correction Model,” Review of Financial Economics, Vol. 12, pp.301-313.
10.	Kilian, L., (2009), “Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market,” American Economic Review, Vol. 99, pp.1053-1069.
11.	Kocaarslan, B., R. Sari, A. Gormus, and U. Soytas, (2017), “Dynamic Correlations between BRIC and U.S. Stock Markets: The Asymmetric Impact of Volatility Expectations in Oil, Gold and Financial Markets,” Journal of Commodity Markets, Vol. 7, pp.41-56.
12.	Katsiampa, P.. S. Corbet, and B. Lucey, (2019), “Volatility Spillover Effects in Leading Cryptocurrencies: A BEKKMGARCH Analysis,” Finance Research Letters, Vol. 29, pp.68-74.
13.	Miller, J. I., and A. Ratti, (2009), “Crude Oil and Stock Markets: Stability, Instability, and Bubbles,” Energy Economics, Vol. 31, pp.559-568.
14.	Narayan, P. K., and S. S. Sharma, (2011), “New Evidence on Oil Price and Firm Returns,” Journal of Banking and Finance, Vol. 35, pp.3253-3262.
15.	Nguyen, C., M. Bhatti, M. Komorníková, and J. Komorník, (2016), “Gold Price and Stock Markets Nexus under Mixed-Copulas,” Economic Modelling, Vol. 58, pp.283-292.
16.	Pan, M. S., R. Wing, and Y. Liu, (2007), “Dynamic Linkages Between Exchange Rates and Stock Prices: Evidence from East Asian Markets,” International Review of Economics and Finance, Vol. 16, pp.503-520.
17.	Park, J., and R. A. Ratti, (2008), “Oil Price Shocks and Stock Markets in the U.S. and 13 European Countries,” Energy Economics, Vol. 30, pp.2587-2608.
18.	Peng, C. L., C. F. Chung, C. C. Tsai, and C. T. Wang, (2017), “Exploring the Returns and Volatility Spillover Effect in Taiwan and Japan Stock Markets,” Asian Economic and Financial Review, Vol. 7, pp.175-187.
19.	Sadorsky, P., (1999), “Oil Price Shocks and Stock Market Activity,” Energy Economics, Vol. 21, pp.449-469.
20.	Tsai, I. C., (2012), “The Relationship Between Stock Price Index and Exchange Rate in Asian Markets: A Quantile Regression Approach,” Journal of International Financial Markets, Institutions & Money, Vol. 22, pp.609-621.
21.	Tsagkanos, A., and C. Siriopoulos, (2013), “A Long-Run Relationship Between Stock Price Index and Exchange Rate: A Structural Nonparametric Cointegrating Regression Approach,” Journal of International Financial Markets, Institutions and Money, Vol. 25, pp.106-118.
22.	Teulon, F., and K. Guesmi, (2014), “Dynamic Spillover Between The Oil And Stock Markets Of Emerging Oil-Exporting Countries,” The Journal of Applied Business Research, Vol. 30, pp.1-9.
23.	Tursoy, T., and F. Faisal, (2018), “The Impact of Gold and Crude Oil Prices on Stock Market in Turkey: Empirical Evidences from ARDL Bounds Test and Combined Cointegration,” Resources Policy, Vol. 55, pp.49-54.
24.	Wen, X., and H. Cheng, (2018), “Which is the Safe Haven for Emerging Stock Markets, Gold or the US Dollar?,” Emerging Markets Review, Vol. 35, pp.69-90.
25.	Zhang, Y. J., Y. Fan, H. T. Tsai, and Y. M. Wei, (2008), “Spillover Effect of US Dollar Exchange Rate on Oil Prices,” Journal of Policy Modeling, Vol. 30, pp.973-991.
26.	Živkov, D., J. Njegić, and M. Momčilović, (2018), “Bidirectional Spillover Effect Between Russian Stock Index and the Selected Commodities,” Zb. rad. Ekon. fak. Rij., Vol. 36, pp.27-51.