Please use this identifier to cite or link to this item:

Title: 探討跨原油市場與國際金融市場間之外溢效果-運用GARCH-MIDAS模型
Exploring the spillover effect across crude oil market and international financial markets — Empirical evidence from GARCH-MIDAS analysis
Authors: 黃毅鵬
Huang, Yi-Peng
Contributors: 林靖

Lin, Jing
Shaw, Ming-Fu

Huang, Yi-Peng
U.S-China trade war
spillover effect
long-term volatility
Date: 2021
Issue Date: 2021-09-02 17:44:52 (UTC+8)
Abstract: 原油為石化產業基礎原料,原油價格波動反映全球經濟活動,受到各國政府與投資人關心。不論是各國政府制定能源政策亦或是民生經濟政策方面皆為重要的參考準則,投資人則注重在對原油價格波動的避險投資策略。目前,全球接連面臨美中貿易戰與COVID-19疫情,COVID-19疫情期間油價波動劇烈,更加深本研究對原油價格波動之探討。本研究運用GARCH-MIDAS模型實證分析2016年1月4日至2021年2月26日跨原油市場與國際金融市場間之外溢效果,全樣本期間涵蓋美中貿易戰與COVID-19疫情兩個經濟事件。原油市場以西德州原油期貨為研究變數,國際金融市場分為航運金融市場、農產品市場、外匯市場、成品油市場、生質燃料市場與塑膠原料市場,作為對原油市場長期波動外溢效果之傳遞市場。實證顯示航運金融市場、外匯市場、成品油市場、生質燃料市場、塑膠原料市場在美中貿易戰延伸COVID-19期間顯著,說明經濟事件發生時,這些市場變數的已實現波動率能夠預測長期西德州原油期貨波動率。農產品市場在過去12個月COVID-19疫情期間顯著,說明全球疫情產生糧食衝擊外溢至原油市場。塑膠原料市場變數在所有樣本內皆顯著對原油市場產生外溢效果,說明石化中下游產業的動盪會對於原油市場價格波動影響。近年來,在中國石化產業的強大基礎下,發展出許多衍生性金融商品。在過去研究文獻中較少探討到塑膠商品期貨與原油價格波動之間的關係。藉由本研究發現,提供未來原油相關研究更深入探討石化產業鏈中下游的研究缺口。
Crude oil is the raw material for petrochemical industry. The fluctuation of oil price is concerned by governments and investors. It reflects on global economic activities. So far, the world has encountered the US-China trade war and the COVID-19 pandemic one after another. Due to oil price volatile greatly during the COVID-19 pandemic, it deepens the purpose of exploring oil price volatility in this study. This paper applied the GARCH-MIDAS model to explore the spillover effect across crude oil market and international financial markets from January 4, 2016 to February 26, 2021. The full sample covered two economic events of the U.S-China trade war and the COVID-19 pandemic. The international financial markets consist of freight market, agricultural product market, foreign exchange market, refined oil market, biofuel market and plastic market in this research. The results reveal that freight market, foreign exchange market, refined oil market, biofuel market and plastic market are significant during the U.S-China trade war and COVID-19 pandemic. It indicates that the realized volatility of these market’s variables can predict WTI futures volatility in the long term when economic events occurred. Agricultural product market had significant influence on oil market during the COVID-19 pandemic in the last 12 months. It shows that global pandemic caused food shocks and transmitted volatility to crude oil market. Plastic market significantly transmitted volatility to crude oil market in all samples, indicating that the shock of the midstream and downstream of the petrochemical industry had an impact on crude oil market. Recently, many financial derivatives about petrochemical industry have been launched in China. The relationship between plastic commodity futures and crude oil market is less discussed in previous literatures. Finally, this study further provides the research gap to explore the middle and lower reaches of the petrochemical industry chain about oil-related research.
Reference: Adland, R., & Cullinane, K. (2006). The non-linear dynamics of spot freight rates in tanker markets. Transportation Research Part E: Logistics and Transportation Review, 42(3), 211-224.
Algieri, B., & Leccadito, A. (2017). Assessing contagion risk from energy and non-energy commodity markets. Energy Economics, 62, 312-322.
Alizadeh, A., & Nomikos, N. (2009). Shipping derivatives and risk management. Springer.
Alizadeh, A. H., & Nomikos, N. K. (2004). Cost of carry, causality and arbitrage between oil futures and tanker freight markets. Transportation Research Part E: Logistics and Transportation Review, 40(4), 297-316.
Alizadeh, A. H., Huang, C. Y., & van Dellen, S. (2015). A regime switching approach for hedging tanker shipping freight rates. Energy Economics, 49, 44-59.
Alizadeh, A. H., Nomikos, N. K., & Pouliasis, P. K. (2008). A Markov regime switching approach for hedging energy commodities. Journal of Banking & Finance, 32(9), 1970-1983.
Asgharian, H., Hou, A. J., & Javed, F. (2013). The importance of the macroeconomic variables in forecasting stock return variance: A GARCH‐MIDAS approach. Journal of Forecasting, 32(7), 600-612.
Asgharian, H., Christiansen, C., & Hou, A. J. (2015). Effects of macroeconomic uncertainty on the stock and bond markets. Finance Research Letters, 13, 10-16.
Atil, A., Lahiani, A., & Nguyen, D. K. (2014). Asymmetric and nonlinear pass-through of crude oil prices to gasoline and natural gas prices. Energy Policy, 65, 567-573.
Bakas, D., & Triantafyllou, A. (2018). The impact of uncertainty shocks on the volatility of commodity prices. Journal of International Money and Finance, 87, 96-111.
Bakas, D., & Triantafyllou, A. (2020). Commodity price volatility and the economic uncertainty of pandemics. Economics Letters, 193, 109283.
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327.
Chang, C. L., McAleer, M., & Tansuchat, R. (2010). Analyzing and forecasting volatility spillovers, asymmetries and hedging in major oil markets. Energy Economics, 32(6), 1445-1455.
Chang, C. L., McAleer, M., & Tansuchat, R. (2011). Crude oil hedging strategies using dynamic multivariate GARCH. Energy Economics, 33(5), 912-923.
Chang, H. F., Huang, L. C., & Chin, M. C. (2013). Interactive relationships between crude oil prices, gold prices, and the NT–US dollar exchange rate—A Taiwan study. Energy policy, 63, 441-448.
Chang, T. H., & Su, H. M. (2010). The substitutive effect of biofuels on fossil fuels in the lower and higher crude oil price periods. Energy, 35(7), 2807-2813.
Chen, F., Miao, Y., Tian, K., Ding, X., & Li, T. (2017). Multifractal cross-correlations between crude oil and tanker freight rate. Physica A: Statistical Mechanics and its Applications, 474, 344-354.
Chen, S., Meersman, H., & Van de Voorde, E. (2010). Dynamic interrelationships in returns and volatilities between Capesize and Panamax markets. Maritime Economics & Logistics, 12(1), 65-90.
Chiu, F. P., Hsu, C. S., Ho, A., & Chen, C. C. (2016). Modeling the price relationships between crude oil, energy crops and biofuels. Energy, 109, 845-857.
Chong, J., & Miffre, J. (2010). Conditional return correlations between commodity futures and traditional assets. Journal of Alternative Investments, 12(3), 61-75.
Conrad, C., Custovic, A., & Ghysels, E. (2018). Long-and short-term cryptocurrency volatility components: A GARCH-MIDAS analysis. Journal of Risk and Financial Management, 11(2), 23.
Conrad, C., & Loch, K. (2015). The variance risk premium and fundamental uncertainty. Economics Letters, 132, 56-60.
Demirer, R., Kutan, A. M., & Shen, F. (2012). The effect of ethanol listing on corn prices: Evidence from spot and futures markets. Energy economics, 34(5), 1400-1406.
Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366a), 427-431.
Diebold, F. X., & Yilmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. The Economic Journal, 119(534), 158-171.
Diebold, F. X., & Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of forecasting, 28(1), 57-66.
Ding, L., & Vo, M. (2012). Exchange rates and oil prices: A multivariate stochastic volatility analysis. The Quarterly Review of Economics and Finance, 52(1), 15-37.
Drobetz, W., Richter, T., & Wambach, M. (2012). Dynamics of time-varying volatility in the dry bulk and tanker freight markets. Applied financial economics, 22(16), 1367-1384.
Drobetz, W., Schilling, D., & Tegtmeier, L. (2010). Common risk factors in the returns of shipping stocks. Maritime Policy & Management, 37(2), 93-120.
Du, X., Cindy, L. Y., & Hayes, D. J. (2011). Speculation and volatility spillover in the crude oil and agricultural commodity markets: A Bayesian analysis. Energy Economics, 33(3), 497-503.
Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the econometric society, 987-1007.
Engle, R. F., Ghysels, E., & Sohn, B. (2013). Stock market volatility and macroeconomic fundamentals. Review of Economics and Statistics, 95(3), 776-797.
Engle, R. F., & Rangel, J. G. (2008). The spline-GARCH model for low-frequency volatility and its global macroeconomic causes. The review of financial studies, 21(3), 1187-1222.
Ewing, B. T., Malik, F., & Ozfidan, O. (2002). Volatility transmission in the oil and natural gas markets. Energy Economics, 24(6), 525-538.
Fang, T., Lee, T. H., & Su, Z. (2020). Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection. Journal of Empirical Finance, 58, 36-49.
Ghysels, E., Santa-Clara, P., & Valkanov, R. (2005). There is a risk-return trade-off after all. Journal of Financial Economics, 76(3), 509-548.
Ghysels, E., Sinko, A., & Valkanov, R. (2007). MIDAS regressions: Further results and new directions. Econometric reviews, 26(1), 53-90.
Girardin, E., & Joyeux, R. (2013). Macro fundamentals as a source of stock market volatility in China: A GARCH-MIDAS approach. Economic Modelling, 34, 59-68.
Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The journal of finance, 48(5), 1779-1801.
Gorton, G., & Rouwenhorst, K. G. (2006). Facts and fantasies about commodity futures. Financial Analysts Journal, 62(2), 47-68.
Gu, F., Wang, J., Guo, J., & Fan, Y. (2020). Dynamic linkages between international oil price, plastic stock index and recycle plastic markets in China. International Review of Economics & Finance, 68, 167-179.
Jadidzadeh, A., & Serletis, A. (2017). How does the US natural gas market react to demand and supply shocks in the crude oil market?. Energy Economics, 63, 66-74.
Ji, Q., & Fan, Y. (2011). A dynamic hedging approach for refineries in multiproduct oil markets. Energy, 36(2), 881-887.
Ji, Q., Zhang, X., & Zhu, Y. (2020). Multifractal analysis of the impact of US–China trade friction on US and China soy futures markets. Physica A: Statistical Mechanics and its Applications, 542, 123222.
Jiang, J., Marsh, T. L., & Tozer, P. R. (2015). Policy induced price volatility transmission: Linking the US crude oil, corn and plastics markets. Energy economics, 52, 217-227.
Junttila, J., Pesonen, J., & Raatikainen, J. (2018). Commodity market based hedging against stock market risk in times of financial crisis: The case of crude oil and gold. Journal of International Financial Markets, Institutions and Money, 56, 255-280.
Kang, S. H., McIver, R., & Yoon, S. M. (2017). Dynamic spillover effects among crude oil, precious metal, and agricultural commodity futures markets. Energy Economics, 62, 19-32.
Karali, B., & Ramirez, O. A. (2014). Macro determinants of volatility and volatility spillover in energy markets. Energy Economics, 46, 413-421.
Kavussanos, M. G., & Visvikis, I. D. (2006). Shipping freight derivatives: a survey of recent evidence. Maritime Policy & Management, 33(3), 233-255.
Kavussanos, M. G., & Nomikos, N. K. (1999). The forward pricing function of the shipping freight futures market. Journal of Futures Markets: Futures, Options, and Other Derivative Products, 19(3), 353-376.
Kilian, L. (2009). Not all oil price shocks are alike: Disentangling demand and supply shocks in the crude oil market. American Economic Review, 99(3), 1053-69.
Lee, T. K., & Zyren, J. (2007). Volatility relationship between crude oil and petroleum products. Atlantic Economic Journal, 35(1), 97-112.
Lee, Y. H., Hu, H. N., & Chiou, J. S. (2010). Jump dynamics with structural breaks for crude oil prices. Energy Economics, 32(2), 343-350.
Li, K. X., Qi, G., Shi, W., Yang, Z., Bang, H. S., Woo, S. H., & Yip, T. L. (2014). Spillover effects and dynamic correlations between spot and forward tanker freight markets. Maritime Policy & Management, 41(7), 683-696.
Lin, A. J., & Chang, H. Y. (2020). Volatility transmission from equity, bulk shipping, and commodity markets to oil ETF and energy fund—A GARCH-MIDAS model. Mathematics, 8(9), 1534.
Lin, F. L., Chen, Y. F., & Yang, S. Y. (2016). Does the value of US dollar matter with the price of oil and gold? A dynamic analysis from time–frequency space. International Review of Economics & Finance, 43, 59-71.
Ljung, G. M., & Box, G. E. (1978). On a measure of lack of fit in time series models. Biometrika, 65(2), 297-303.
Ma, Y., Duan, Q., & Wu, H. (2021). Does a stock's name affect its return? Evidence from the Chinese stock market during the China–US trade conflict. Finance Research Letters, 40, 101733.
Masih, M., Algahtani, I., & De Mello, L. (2010). Price dynamics of crude oil and the regional ethylene markets. Energy Economics, 32(6), 1435-1444.
McPhail, L. L. (2011). Assessing the impact of US ethanol on fossil fuel markets: A structural VAR approach. Energy Economics, 33(6), 1177-1185.
Mitchell, D. (2008). A note on rising food prices. World bank policy research working paper, (4682).
Mo, B., Nie, H., & Jiang, Y. (2018). Dynamic linkages among the gold market, US dollar and crude oil market. Physica A: Statistical Mechanics and its Applications, 491, 984-994.
Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the Econometric Society, 347-370.
Niu, H., & Wang, J. (2017). Return volatility duration analysis of NYMEX energy futures and spot. Energy, 140, 837-849.
Pan, Z., Wang, Y., & Yang, L. (2014). Hedging crude oil using refined product: A regime switching asymmetric DCC approach. Energy economics, 46, 472-484.
Perron, P., & Phillips, P. C. (1987). Does GNP have a unit root?: A re-evaluation. Economics Letters, 23(2), 139-145.
Poulakidas, A., & Joutz, F. (2009). Exploring the link between oil prices and tanker rates. Maritime Policy & Management, 36(3), 215-233.
Prokopczuk, M., Stancu, A., & Symeonidis, L. (2019). The economic drivers of commodity market volatility. Journal of International Money and Finance, 98, 102063.
Reboredo, J. C., Rivera-Castro, M. A., & Zebende, G. F. (2014). Oil and US dollar exchange rate dependence: A detrended cross-correlation approach. Energy Economics, 42, 132-139.
Ruan, Q., Wang, Y., Lu, X., & Qin, J. (2016). Cross-correlations between Baltic Dry Index and crude oil prices. Physica A: Statistical Mechanics and its Applications, 453, 278-289.
Said, S. E., & Dickey, D. A. (1984). Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika, 71(3), 599-607.
Salisu, A. A., & Mobolaji, H. (2013). Modeling returns and volatility transmission between oil price and US-Nigeria exchange rate. Energy Economics, 39, 169-176.
Serletis, A., & Xu, L. (2019). The ethanol mandate and crude oil and biofuel agricultural commodity price dynamics. Journal of Commodity Markets, 15, 100068.
Suenaga, H., & Smith, A. (2011). Volatility dynamics and seasonality in energy prices: Implications for crack-spread price risk. The Energy Journal, 32(3).
Suenaga, H., Smith, A., & Williams, J. (2008). Volatility dynamics of NYMEX natural gas futures prices. Journal of Futures Markets: Futures, Options, and Other Derivative Products, 28(5), 438-463.
Sukcharoen, K., & Leatham, D. J. (2017). Hedging downside risk of oil refineries: A vine copula approach. Energy Economics, 66, 493-507.
Tsouknidis, D. A. (2016). Dynamic volatility spillovers across shipping freight markets. Transportation Research Part E: Logistics and Transportation Review, 91, 90-111.
Uddin, G. S., Tiwari, A. K., Arouri, M., & Teulon, F. (2013). On the relationship between oil price and exchange rates: A wavelet analysis. Economic Modelling, 35, 502-507.
Vacha, L., & Barunik, J. (2012). Co-movement of energy commodities revisited: Evidence from wavelet coherence analysis. Energy Economics, 34(1), 241-247.
Wang, L., Ma, F., Liu, J., & Yang, L. (2020). Forecasting stock price volatility: New evidence from the GARCH-MIDAS model. International Journal of Forecasting, 36(2), 684-694.
Wang, Y., Liu, L., & Wu, C. (2017). Forecasting the real prices of crude oil using forecast combinations over time-varying parameter models. Energy Economics, 66, 337-348.
Wang, Y. S., & Chueh, Y. L. (2013). Dynamic transmission effects between the interest rate, the US dollar, and gold and crude oil prices. Economic Modelling, 30, 792-798.
Wang, Y., & Wu, C. (2013). Efficiency of crude oil futures markets: new evidence from multifractal detrending moving average analysis. Computational Economics, 42(4), 393-414.
Wei, Y., Yu, Q., Liu, J., & Cao, Y. (2018). Hot money and China’s stock market volatility: Further evidence using the GARCH–MIDAS model. Physica A: Statistical Mechanics and Its Applications, 492, 923-930.
Xu, Y., & Lien, D. (2020). Dynamic exchange rate dependences: The effect of the US-China trade war. Journal of International Financial Markets, Institutions and Money, 68, 101238.
Ye, M., Zyren, J., & Shore, J. (2002). Forecasting crude oil spot price using OECD petroleum inventory levels. International Advances in Economic Research, 8(4), 324-333.
Yue-Jun Zhang , Ying Fan , Hsien-Tang Tsai , Yi-Ming Wei. (2008). Spillover effect of US dollar exchange rate on oil prices. Journal of Policy Modeling , 30 , 973-991.
Zakoian, J. M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and control, 18(5), 931-955.
Zhang, Y. J., Fan, Y., Tsai, H. T., & Wei, Y. M. (2008). Spillover effect of US dollar exchange rate on oil prices. Journal of Policy modeling, 30(6), 973-991.
Zhou, Z., Fu, Z., Jiang, Y., Zeng, X., & Lin, L. (2020). Can economic policy uncertainty predict exchange rate volatility? New evidence from the GARCH-MIDAS model. Finance Research Letters, 34, 101258.
Description: 碩士
Source URI:
Data Type: thesis
Appears in Collections:[經濟學系] 學位論文

Files in This Item:

File Description SizeFormat
803901.pdf4089KbAdobe PDF0View/Open

All items in 學術集成 are protected by copyright, with all rights reserved.

社群 sharing