在金融全球化之浪潮下，能源類商品成為重要避險工具，但隨著商品市場金融化現象加劇，能源類商品是否能夠成為危機發生時之避風港，是本文想要探討之議題。本研究選擇原油報酬做為能源商品市場之代表變數，利用金融壓力指數顯示金融市場不確定性，再以Engle (2002) DCC-GARCH模型估計兩市場隨時間變化之條件相關係數為被解釋變數，以1997年亞洲金融危機、2007年全球金融危機、2010年歐洲債務危機等三次金融危機，所設定之虛擬變數為解釋變數，討論商品市場與金融市場之間的連動關係，分析金融市場的不確定是否透過傳染之途徑影響商品市場價格的變化。實證結果顯示，傳染的現象僅於2007年全球金融危機時出現，表示當全球性危機發生時，原油商品與期貨無法達到充分避險；而於另外兩次金融危機中，則沒有足夠證據顯示原油商品可作為資產之避風港。

Under financial globalization, energy commodity becomes an important diversification instrument. However, according to financialization in commodity markets intensifying, energy commodity providing an asset refuge during crash period is arguable. One of main purpose of this paper is to prove statistically whether the diversifying feature exists among energy commodity. This paper applies DCC-GARCH model published by Engle (2002), as crude oil return represents the independent variable from energy market and financial stress index measures the uncertainty from financial market, to estimate the dynamic conditional correlation. We choose three recent crises, namely Asia Financial Crisis in 1997, Global Financial Crisis in 2007, and Euro-zone Debts Crisis in 2010, to discuss interaction across two markets and analyze whether the financial uncertainty affects the commodity markets via contagion. Adding crisis dummy variables in regression model, it enables us to compare the conditional correlation between tranquil period and turmoil period to test the existence of contagion. Our empirical results suggest that contagion is only statistically significant during Global Financial Crisis, which interprets that crude oil commodity and future are not refuge during crash period. Further, the other two crises dummy variables are not statically significant implying there is no sufficient evidence to show crude oil commodity can be an asset refuge when crises happen.

目錄

第一章 緒論	1
第一節 研究背景與動機	1
第二節 研究目的	2
第三節 研究方法	3
第四節 章節架構	5
第二章 文獻回顧	6
第一節 金融壓力指數	6
第二節 能源商品市場與金融市場	8
第三章 實證模型與分析	14
第一節 模型設定	14
第一項 自我相關條件異質變異模型 (ARCH)	14
第二項 ARCH效果檢定 (ARCH effect test)	15
第三項 一般化自我相關條件異質變異模型(GARCH)	16
第四項 多變量GARCH (Multivariate GARCH)	16
第五項 金融危機時間界定與虛擬變數 (dummy)	18
第六項 傳染之風險分析	23
第二節 資料來源與說明	23
第一項 金融指數之介紹	25
第三節 實證結果	26
第四章 結論	38
參考文獻	41
圖目錄

圖一：研究流程圖	4
圖二：研究架構圖	5
圖三：原始變數資料之時間趨勢	19
圖四：兩市場之動態相關係數	29

 
表目錄

表一：衡量金融市場不確定性之金融指數	8
表二：金融危機起訖時間	19
表三：日資料變數說明與整理	24
表四：週資料變數說明與整理	24
表五：月資料變數說明與整理	24
表六：合併後資料之期間與樣本數	25
表七：各變數之敘述統計量	28
表八：各變數之ARCH-LM檢定結果	29
表九：放入三個虛擬變數之迴歸結果	34
表十：僅取AGFC為虛擬變數之迴歸結果	35
表十一：將兩市場風險作為自變數之迴歸結果	37

參考文獻
1.	高崇瑋、萬哲鈺 (2014)，「臺灣之利率轉嫁分析」，《臺灣經濟預測與政策》，44，45-101。
2.	Bain, C. (2014), “What Next for Commodity Price Correlations?” Capital Economics, London
3.	Bicchetti, D. and N. Maystre (2013), “The synchronized and long-lasting structural change on commodity markets: evidence from high frequency data,” Algorithmic Finance, 2(3-4), 233-239.
4.	Bekaert, G., C. Harvey and A. Ng (2005), “Market integration and contagion,” Journal of Business, 78, 39-69
5.	Burbidge, J. and A. Harrison (1984), “Testing for the effects of oil-price rises using vector autoregressions,” International Economic Review, 459-484.
6.	Bruno, M. and J. Sachs (1982), “Energy and resource allocation: A dynamic model of the Dutch Disease,” The Review of Economic Studies 49, 845-859.
7.	Baker, S. R., N. Bloom and S. J. Davis (2016), “Measuring economic policy uncertainty,” The Quarterly Journal of Economics, 131(4), 1593-1636.
8.	Bollerslev, T. (1986), “Generalized autoregressive conditional heteroscedasticity,” Journal of econometrics, 31(3), 307-327.
9.	Bodie, Z. (1976), “Common stocks as a hedge against inflation,” The journal of finance, 31(2), 459-470.
10.	Bodie, Z. and V. I. Rosansky (1980), “Risk and return in commodity futures,” Financial Analysts Journal, 36(3), 27-39.
11.	Creti, A., M. Joëts, and V. Mignon (2013), “On the links between stock and commodity markets' volatility,” Energy Economics, 37, 16-28.
12.	Caporale, G. M., A. Cipollini and N. Spagnolo (2005), “Testing for contagion: a conditional correlation analysis,” Journal of Empirical Finance, 12(3), 476-489.
13.	Cheng, I. H., A. Kirilenko and W. Xiong (2015), “Convective risk flows in commodity futures markets,” Review of Finance, 19(5), 1733-1781.
14.	Chiang, T. C., B.N. Jeon and H. Li (2007), “Dynamic correlation analysis of financial contagion: Evidence from Asian markets,” Journal of International Money and Finance 26, 1206-1228.
15.	Delatte, A. L. and C. Lopez (2013), “Commodity and equity markets: Some stylized facts from a copula approach,” Journal of Banking & Finance, 37(12), 5346-5356.
16.	Du, L., H. Yanan, and C. Wei (2010), “The relationship between oil price shocks and China’s macro-economy: An empirical analysis,” Energy Policy 38(8), 4142-4151.
17.	Dua, P., and D. Tuteja (2016), “Financial crises and dynamic linkages across international stock and currency markets,” Economic Modelling, 59, 249-261.
18.	Engle, R. F. (1982), “Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation,” Econometrica: Journal of the Econometric Society, 987-1007.
19.	Engle, R. E. (2002), “Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models,” Journal of Business & Economic Statistics 20, 339-350.
20.	Fama, E. F. (1981), “Stock returns, real activity, inflation, and money,” The American Economic Review, 71(4), 545-565.
21.	Forbes, K. and R. Rigobon (2001), “Measuring contagion: conceptual and empirical issues,” In S. Claessens and K Forbes (eds.), International financial contagion. Boston: Kluwer Academic.
22.	Forbes, K. and R. Rigobon (2002), “No contagion, only interdependence: measuring stock market co-movements,” The journal of Finance, 57(5), 2223-2261.
23.	Fry-McKibbin, R., C. Y.-L. Hsiao and C. Tang (2014), “Contagion and Global Financial Crises: Lessons from nine crisis episodes,” Open Economies Review, 25, 521-570.
24.	Girardi, D. (2012), “Do financial investors affect the price of wheat?” PSL Q. Rev. 65 (260), 79-109.
25.	Gisser, M., and T. H. Goodwin (1986), “Crude oil and the macroeconomy: Tests of some popular notions: Note,” Journal of Money, Credit and Banking 18 (1), 95-103.
26.	Henderson, B.J., N.D. Pearson and W. Li (2012), “New Evidence on the Financialization of Commodity Markets,” Working paper. Georgetown University of Illinois.
27.	Hamilton, J. D. (1983), ”Oil and the macroeconomy since World War II,” The Journal of Political Economy 38 (2), 228-248.
28.	Kenourgios, D., Samitas, A., and Paltalidis, N. (2011), “Financial crises and stock market contagion in a multivariate time-varying asymmetric framework,” Journal of International Financial Markets, Institutions and Money, 21(1), 92-106.
29.	Mork, K. A. (1989), “Oil and the macroeconomy when prices go up and down: An extension of Hamilton’s results,” Journal of Political Economy 97 (3), 740-744.
30.	Nagayev, R., M. Disli, K. Inghelbrecht and A. Ng (2016), “On the dynamic links between commodities and Islamic equity,” Energy Economics, 58, 125-140.
31.	Nazlioglu, S., C. Erdem, and U. Soytas (2013), “Volatility spillover between oil and agricultural commodity markets,” Energy Economics 36, 658-665
32.	Nazlioglu, S., U. Soytas and R. Gupta (2015), “Oil prices and financial stress: A volatility spillover analysis,” Energy Policy, 82, 278-288.
33.	Ordu, B. M., & U. Soytaş. (2016), ”The Relationship Between Energy Commodity Prices and Electricity and Market Index Performances: Evidence from an Emerging Market,” Emerging Markets Finance and Trade, 52(9), 2149-2164.
34.	Reboredo, J. C., and Uddin, G. S. (2016), “Do financial stress and policy uncertainty have an impact on the energy and metals markets? A quantile regression approach,” International Review of Economics & Finance, 43, 284-298.
35.	Rigobon, R. (2003), “On the measurement of the international propagation of shocks: is the transmission stable?” Journal of International Economics 61, 261-283.
36.	Rajwani, S. and D. Kumar (2015), “A Dynamic Conditional Correlation Analysis-Based Approach to Test Financial Contagion in Developing Markets,” In S. Chatterjee, N. P. Singh, D. P. Goyal and N. Gupta (eds.), Managing in Recovering Markets. Springer India, 1-13.
37.	Silvennoinen, A., and S. Thorp (2013), “Financialization, crisis and commodity correlation dynamics,” Journal of International Financial Markets, Institutions and Money, 24, 42-65.
38.	Schmukler, S. L. (2004), “Benefits and risks of financial globalization: challenges for developing countries,” Globalization, Growth, and Poverty, World Bank Policy Research Report.
39.	Turhan, I., E. Hacihasanoglu and U. Soytas (2013), “Oil prices and emerging market exchange rates,” Emerging Markets Finance and Trade, 49(sup1), 21-36. doi:10.2753/REE1540-496X4901S102
40.	Teterin, P., Brooks, R., and Enders, W. (2016), “Smooth volatility shifts and spillovers in US crude oil and corn futures markets,” Journal of Empirical Finance, 38, 22-36.
41.	van Horen, N., H. Jager and F. Klaassen (2006), “Foreign exchange market contagion in the Asian Crisis: A regression-based approach,” Review of World Economics, 142, 374-401