原油與金融市場之波動傳染

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。

關鍵字

原油報酬；金融壓力指數；波動傳染；條件相關係數； DCC-GARCH

並列摘要

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.

並列關鍵字

oil return ； financial stress index ； volatility contagion ； conditional correlation ； DCC-GARCH

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。

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.

國際替代計量

原油與金融市場之波動傳染

全文下載

主題瀏覽