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系統識別號 U0002-1801200921515200
中文論文名稱 國際原油價格波動主因之研究
英文論文名稱 Essays on Primary Factors Affecting Volatility of Crude Oil Prices
校院名稱 淡江大學
系所名稱(中) 產業經濟學系博士班
系所名稱(英) Department of Industrial Economics
學年度 97
學期 1
出版年 98
研究生中文姓名 李怡慧
研究生英文姓名 Yi-Huey Lee
學號 893510064
學位類別 博士
語文別 中文
口試日期 2008-12-25
論文頁數 97頁
口試委員 指導教授-廖惠珠
委員-張四立
委員-楊浩彥
委員-趙志凌
委員-廖惠珠
委員-萬哲鈺
中文關鍵字 油價  波動  EKC  電子交易  中國能源需求  分量迴歸  結構轉變  縱橫資料法 
英文關鍵字 Oil price  Volatility  EKC  Electronic trade  Chinese energy demand  Structural change  Quantile regression  Panel data 
學科別分類 學科別社會科學經濟學
中文摘要 近年來國際原油價格變化萬千且波動甚劇,使得國際油價成為全球矚目的焦點。因此,本研究將從全球暖化、期貨市場交易行為以及市場供需之變化等層面,分析EKC、電子交易制度以及中國大陸石油需求對國際原油價格之影響。
全球暖化問題造成各地氣候異常,進而影響國際油價走勢,故本論文第二章採用分量迴歸分析法,探討經濟成長與溫室氣體 (CO2) 排放的關係,希冀藉此能掌握全球暖化情勢,並可捕捉油價未來之動態。研究結果發現倒U字型的EKC曲線,且在高CO2排放量的國家具有更顯著的關係。除傳統分量迴歸方法外,為避免產生模型選擇誤差之情形,本文亦使用了半參數的分量迴歸方式,並與傳統分量迴歸相比較,結果發現三次式的迴歸方程較適合捕捉CO2的排放與經濟成長之間的關係。
由於電子交易制度日趨普及,且似乎更適合目前快速變遷的世界,基此,本論文第三章探討電子交易制度的施行,對布蘭特原油期貨價格波動的影響。待建立適當的 GARCH 模型後,將可估計出兩條條件報酬波動序列,再應用 Bai 和 Perron 所提出的結構轉變模型,發現報酬波動序列出現兩個明顯的結構中斷點,約是電子交易施行的附近。而實証結果也顯示在全面電子交易時期,條件報酬波動序列主要受到暫時持續性的影響,而非波動聚集效果,此結果隱含在電子交易制度下,人們傾向運用較多完整的訊息。然而,基於原油期貨商品之特殊性質,多數投資者在缺乏充分認知下,無法充分掌握合理之原油商品價格,故在電子交易制度之下,使得原油期貨價格波動程度提高。
有鑑於中國大陸石油需求日益增加,因此,本研究第四章以縱橫資料分析法,探討1999年至2006年間,中國各省市級行政區域之汽、柴油需求發展狀況。研究結果發現,汽、柴油價格對於汽、柴需求幾乎無任何影響性,另外,隨著國民所得的增加,各地都市化、工業化以及汽車化的興起,帶動對柴油與汽油的需求。未來中國大陸為滿足內部成品油的需求,新建置的煉油廠將陸續投入生產,故需進口大量原油方得以煉製足夠成品油,此隱含將有一股來自需求面的向上拉力推升國際油價。
英文摘要 The record high crude oil price and its high volatility attract lots of attention in the whole world. This paper attempts to investigate the influences of global warming, futures trading system and oil market demand and supply on oil price. By analyzing the EKC, we can catch the situation of global warming and infer the possible oil price pattern. In contrast to conventional conditional mean approaches, we use the quantile regression, both parametrically and semi-parametrically, to investigate the relationship between CO2 emissions and economic growth. The empirical results show that the cubic form relationship is found to be better capturing this relationship since our semi-parametric result is more consistent to the cubic form of parametric result. We found some evidence to support the inverted- shape EKC again although it is only observed for higher CO2 emissions countries.
Since electronic trading systems are more pervasive today and may be more suitable for the rapid changing world. This paper uses daily Brent crude prices to investigate the employment of electronic trading on the returns conditional volatility in the oil futures market. After a suitable GARCH model is established, the conditional volatility series are found. The Bai and Perron model is then used to find two significant structural breaks for these conditional volatility series around two implementation dates of electronic trading. This result indicates the change in the trading system has significant impacts on the returns volatility since our estimated second break date is very close to the all-electronic trade implementation date. Moreover, the conditional volatility in the all-electronic trading period is found to be more dominated by the temporal persistence rather than the volatility clustering effect. All these evidence can shed some light for explaining the high relationship between more volatile world oil price and the more popular electronic trade.
Owning to Chinese oil demand is the major sources of world oil demand growth, this paper would like to investigate the development of Chinese oil demand and infer its possible future trend. It is believed that the oil price trend can be better captured after we trace this oil demand movement. By panel data method, we find the higher GDP growth rate results in more diesel and gasoline consumption, which indicating the increasing consumption of transportation fuels verifies the trend of more motor vehicle population in China. Since GDP is still increasing significantly in China, the trend of more motor vehicle population implies the more demand side pull-up impact on the world oil market especially for the diesel and gasoline market.
論文目次 第一章 緒論...............................................1
1.1 研究動機與目的……………………………………………1
1.2 研究架構……………………………………………………5
1.3 研究內容……………………………………………………5

第二章 以分量迴歸法重新探討環境Kuznets假說……………………7
2.1 前言…………………………………………………………7
2.2 模型設定……………………………………………………9
2.3 資料來源與實證結果………………………………………13
2.4 參數與半母數分量迴歸結果………………………………16
2.5 結論…………………………………………………………23
參考文獻…………………………………………………………………24

第三章 電子交易制度與報酬波動:石油期貨市場之驗證.........27
3.1 前言…………………………………………………………27
3.2 研究方法……………………………………………………31
3.3 資料來源與資料特性………………………………………34
3.4 實證結果……………………………………………………36
3.5 結論…………………………………………………………43
參考文獻…………………………………………………………………45
附錄………………………………………………………………………48

第四章 中國大陸石油需求發展之探討........................49
4.1 前言…………………………………………………………49
4.2 文獻回顧……………………………………………………55
4.3 研究方法……………………………………………………59
4.4 資料來源與說明……………………………………………67
4.5 實證結果……………………………………………………69
4.6 結論…………………………………………………………79
參考文獻…………………………………………………………………82
附錄……………………………………………………………………86

第五章 結論與建議........................................95

表目錄
表 1.1 近年全球原油供需狀況………………………………………2
表 2.1 基本統計量…………………………………………………14
表 2.2 母數與半母數均數迴歸結果………………………………15
表 2.3 含二次項的母數分量迴歸估計結果………………………17
表 2.4 表2.3之ANOVA結果…………………………………………18
表 2.5 含二次與三次項的傳統分量迴歸估計結果………………20
表 2.6 表2.5之ANOVA結果…………………………………………20
表 2.7 半母數分量迴歸估計結果…………………………………22
表 3.1 電子交易系統與公開喊價系統之比較……………………28
表 3.2 基本統計量…………………………………………………35
表 3.3 GARCH 之選定………………………………………………36
表 3.4 GARCH(1,1) 估計結果……………………………………37
表 3.5 結構轉變檢定………………………………………………37
表 3.6 結構中斷日期………………………………………………39
表 3.7 不同區間下GARCH(1,1) 估計結果…………………………43
表 4.1 變數名稱、定義與資料來源………………………………68
表 4.2 汽、柴油需求彈性估計結果………………………………71
表 4.3 人均汽、柴油需求彈性估計結果…………………………72
表 4.4 Panel單根檢定 (含截距趨勢項)…………………………73
表 4.5 Panel單根檢定 (不含趨勢項)……………………………74
表 4.6 汽、柴油消費總量估計結果………………………………76
表 4.7 人均汽、柴油消費量估計結果……………………………77

圖目錄
圖 1.1 2007年全球前六大石油消費國………………………………3
圖 1.2 2007年全球前六大石油進口國………………………………4
圖 1.3 研究架構………………………………………………………5
圖 2.1 CO2與平均每人GDP配適線(二次式與三次式)……………15
圖 2.2 CO2與平均每人GDP配適線(半母數)………………………16
圖 2.3 根據第(2.1)式,在不同分量水準下( =10%、25%、50%、75%和90%),平均每人二氧化碳排放量與平均每人GDP之配適線(二次式)………18
圖 2.4 根據第(2.2)式,在不同分量水準下( =10%、25%、50%、75%和90%),平均每人CO2 排放量與平均每人GDP之配適線(三次式)…………19
圖 2.5 根據第(2.5)式,在不同分量水準下( =10%、25%、50%、75%和90%),平均每人CO2 排放量與平均每人GDP之半母數分量迴歸配適線)…………22
圖 3.1 樣本期間……………………………………………………35
圖 3.2 布蘭特原油期貨每日價格、取自然對數後之報酬與報酬波動趨勢…40
圖 3.3 布蘭特原油期貨每日價格、直接報酬與報酬波動趨勢…41
圖 4.1 全球石油需求與國際油價…………………………………50
圖 4.2 1999-2006年間中國大陸各級行政區域國內生產毛額……51
圖 4.3 1999-2006年間中國大陸各級行政區域汽柴油總消費量…52
圖 4.4 1999-2006年間中國大陸各級行政區域汽柴油價格………52
圖 4.5 1999-2006年間中國大陸各級行政區域人均GDP…………54
圖 4.6 1999-2006年間中國大陸各級行政區域人均汽、柴油消費量…54
圖 4.7 模型設定架構………………………………………………60

參考文獻 Adams, F. G., Shachmurove, Y. 2008. Modeling and forecasting energy consumption in China: implications for Chinese energy demand and imports in 2020. Energy Economics 30, 1263-1278.
Aldy, J.E., 2006. Divergence in state-level per capita carbon dioxide emissions. Land Economics 83(3), 253-369.
Alves, D.C., Bueno, R.L., 2003. Short-run, long-run and cross elasticities of gasoline demand in Brazil. Energy Economics 25, 191-199.
Asafu-Adjaye, J., 2000. The relationship between electricity consumption, electricity prices and economic growth: time series evidence form Asian developing countries. Energy Economics 22, 615-625.
Assaf, A., 2006. The stochastic volatility in mean model and automation: evidence from TSE. The Quarterly Review of Economics and Finance 46 (2), 241-253.
Azomahou, T., Van Phu N., 2001. Economic growth and CO2 emissions: a nonparametric approach. Working paper. Short version is in Journal of Public Economics 2006.
Bai, J., Perron, P., 1998. Estimating and testing linear models with multiple structural changes. Econometrica 66 (1), 47-78.
Bai, J., Perron, P., 2003. Computation and analysis of multiple structural change models. Journal of Applied Econometrics 18 (1), 1-22.
Battalio, R., Hatch, B., Jennings, R., 1997. SOES trading and market volatility. Journal of Financial and Quantitative Analysis 32 (2), 225-238.
Belhaj, M., 2002. Vehicle and fuel Demand in Morocco. Energy Policy 30, 1163-1171.
Bentzen, J., 1994. An empirical analysis of gasoline demand in Demark using cointegration techniques. Energy Economics 16, 139-143.
Bollerslev, T., 1986. Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics 31 (3), 307-327.
Cabedo, J.D., Moya, I., 2003. Estimating oil price ‘value at risk’ using the historical simulation approach. Energy Economics 25 (3), 239-253.
Cheung, K. Y., Thomson, E., 2004. The demand for gasoline in China: a cointegration analysis. Journal of Applied Statistics 31, 533-544.
Colley-Steeley, P., 2005. Noise and the trading mechanism: the case of sets. European Financial Management 11 (3), 387-424.
Coval, J. D., Shumway, T., 2001. Is sound just noise? The Journal of Finance 56 (5), 1887-1910.
Crompton, P., Wu, Y., 2005. Energy consumption in China: past trend and future directions. Energy Economics 27, 195-208.
Culas, R.J., 2007. Deforestation and the environmental Kuznets curve: an institutional perspective. Ecological Economics 61, 429-437.
Dahl, C. A., Sterner, T., 1991. Analyzing gasoline demand elasticities: a survey. Energy Economics 13, 203-210.
Daiglar, R.T., M.K. Wiley, 1999. The impact of trader type on the futures volatility-volume relation. The Journal of Finance 54 (6), 2297-2316.
Deans, A.W., Seaton, J.S., 1999. The regulation of electricity: results from an event study. Applied Economics 31 (5), 609-618.
Dijkgraaf, E., Vollebergh, Herman R.J., 2005. A Test for parameter heterogeneity in CO2 panel EKC estimations. Environmental and Resource Economics 32, 229-239.
Eldor, R., Hauser, S., Pilo, B., Shurki, I., 2006. The contribution of market makers to liquidity and efficiency of options trading in electronic markets. Journal of Banking and Finance 30 (7), 2025-2040.
Eltony, M. N., 1996. Demand for gasoline in the GCC: an application of pooling and testing procedures. Energy Economics 18, 203-209.
Eltony, M. N., Ai-Mutairi, N. H., 1995. Demand for gasoline in Kuwait. Energy Economics 17, 249-253.
Evans, C., 1998. The effects of electronic trading system on open-outcry commodity exchange. Social Science 410, 1-8.
Fleming, J., Ostdiek, B., 1999. The impact of energy derivatives on the crude oil market. Energy Economics 21 (2), 135-167.
Flood, L., Islam N., Sterner, T., 2008. Are demand elasticity affected by politically determined tax levels? simultaneous estimates of gasoline demand and price. Applied Economics Letters, 1-4.
Freund, W.C., Larrain, M., Pagano, M.S., 1997. Market efficiency before and after the introduction of electronic trading at the Toronto Stock Exchange. Review of Financial Economics 6 (1), 29-56.
Freund, W.C., Pagano, M.S., 2000. Market efficiency in specialist markets before and after automation. The Financial Review 35 (3), 79-104.
Garbaccio, R.F., Ho, M.S., Jorgenson, D.W., 1999. Why has the energy-output ratio fallen in China? Energy Journal 20, 63-91.
Ghali H. Khalifa, M.I.T. EI-Dakka, 2004. Energy use and output growth in Canada: a multivariate cointergration analysis. Energy Economics 26, 225-238.
Giot, P., Laurent, S., 2003. Market risk in commodity markets: a VaR approach. Energy Economics 25 (5), 435-457.
Grossman, G. M., Krueger, A. B., 1991. Environmental impacts of a North American Free Trade Agreement. Working paper no. 3914, National Bureau of Economic Research.
Hamilton, J. D., 2001. A parametric approach to flexible nonlinear inference. Econometrica 69, 537-573.
Hamilton, J. D., 2003. What is an oil shock? Journal of Econometrics 113, 363-398.
Holtz-Eakin, D., Selden, T., 1995. Stoking the fires? CO2 emissions and economic growth. Journal of Public Economics 57, 85-101.
Hong, Harrison, 2000. A model of returns and trading in futures markets. The Journal of Finance 55 (2), 959-988.
Horowitz, J. L., Lee, S., 2005. Nonparametric estimation of an additive quantile regression model. Journal of the American Statistical Association 100, 1238-1249.
Huang, River H. C., 2004. A flexible nonlinear inference to the Kuznets hypothesis. Economics Letters 84, 289-296.
Huang, R.H.C., W.H. Cheng, 2005. Tests of the CAPM under structural changes. International Economic Journal 19 (4), 523-541.
Im, K.S., Pesaran, M.H., Shin, Y., 2003 Testing for unit root tests with level shifts. Oxford Bulletin of Economics and Statistics 67, 393-419.
Keii, C., 2000. China’s energy supply and demand situations and coal industry’s trends today. Research Reports 162. Institute of Energy Economics, Japan.
Kriström, B., Lundgren, T., 2005. Swedish CO2-emissions 1900-2010: an exploratory note. Energy Policy 33(9), 1223-30.
Koenker, R., 2005. Quantile Regression, Cambridge University Press, New York, USA.
Koenker, R., Bassett, G., 1978. Regression quantiles. Econometrica 46, 33-50.
Koenker, R., Hallock, K. F., 2001. Quantile regression. Journal of Economic Perspective 15, 143-156.
Lee, C.C., 2005. Energy consumption and GDP in developing countries: a cointegrated panel analysis. Energy Economics 27, 415-427.
Lee, C.C., Chang, C. P., 2008. Energy consumption and economic growth in Asian economics: a more comprehensive analysis using panel data. Resource and Energy Economics 30, 50-65.
Lee, S., 2003. Efficient semiparametric estimation of a partially linear quantile regression model. Econometric Theory 16, 1-31.
Levin, A., Lin, C.F., Chu, C.S., 2002. Unit root tests in panel data: asymptotic and finite-sample properties. Journal of Econometrics 108(1), 1-24.
Liao, H.C., Lee, Y. H., Suen, Y.B., 2008. Electronic trading system and returns volatility in the oil futures market. Energy Economics 30 (5), 2636-2644.
Liao, H.C., Suen, Y. B., 2006. Dating breaks for global crude oil prices and their volatility: a possible price band for global crude prices. Energy Studies Review 14, 189-206.
Lin, X., Polenske, K.R., 1995. Input-output anatomy of China’s energy use changes in the 1980s. Economic Systems Research 7, 67-84.
List, J. A., Craig A. G., 1999. The environmental Kuznets curve: does one size fit all? Ecological Economics 31, 409-423.
MacKinlay, A.C., 1997. Event studies in economics and finance. Journal of Economic Literature 35 (1), 13-39.
Madala, G..S., Wu, S., 1991. A comparative study of unit root tests with panel data and a new sample test. Oxford Bulletin of Economics and Statistics. Special Issue 631-652.
Maghyereh A., 2005. Electronic trading and market efficiency in an emerging market: the case of the Jordanian capital market. Emerging Markets Finance and Trade 41 (4), 5-19.
Massib, M.N., Phelps, B.D., 1994. Electronic trading, market structure and liquidity. Financial Analysts Journal 50 (1), 39-50.
Millimet, D. L., List, J. A., Stengos T., 2003. The environmental Kuznets curve: real progress or misspecified models? The Review of Economics and Statistics 85, 1038-1047.
Monthly Oil Market Report, 2008. Monthly Oil Market Report, December, 2008. OPEC.
National Statistics Bureau of China, 2001–2006. China Statistical Yearbook. China Statistics Press, Beijing.
National Bureau of Statistics of China, 2004–2006. China Energy Statistical Yearbook. China Statistics Press, Beijing.
Nel, W., Cooper, C., 2008. A critical review of IEA’s oil demand forecast for China. Energy Policy 36, 1096-1106.
Perron, P., Z. Qu, 2006. Estimating restricted structural change models. Journal of Econometrics 134 (2), 373-399.
Perron, P., Z. Qu, 2007. Estimating and testing multiple structural changes in multivariate regressions. Econometrica 75 (2), 459-502.
Plourde, A., Watkins, G.C., 1998. Crude oil prices between 1985 and 1994: how volatile in relation to other commodities? Resource and Energy Economics 20 (3), 245-262.
Polemis, L. M., 2006. Empirical assessment of the determinants of road energy demand in Greece. Energy Economics 28, 385-403.
Ramanathan, R., 1999. Short- and long-run elasticities of gasoline demand in India: an empirical analysis using cointegration techniques. Energy Economics 21, 321-330.
Rothman, D.S., 1998. Environmental Kuznets curves - real progress or passing the buck? a case for consumption-based approaches. Ecological Economics 25, 177-194.
Sadorsky, P., 2006. Modeling and forecasting petroleum future volatility. Energy Economics 28 (4), 467-488.
Selden, T., Song, D., 1994. Environmental quality and development: Is there a Kuznets curve for air pollution emissions? Journal of Environmental Economics and Management 27, 147-162.
Shafik, N., 1994. Economic development and environmental quality: an econometric analysis. Oxford Economic Papers 46, 757-773.
Shi, Q., Zhao, J. 1999. Development report of Chinese industries. China ZhiGong Publishing House, Beijing.
Sinton, J.E., Fridley, D.G., 2000. What goes up: recent trends in China’s energy consumption. Energy Policy 28, 671-687.
Skeer, J., Wang, Y., 2007. China on the move: oil price explosion? Energy Policy 35, 678-691.
Stoll, H.R., 2006. Electronic trading in stock markets. Journal of Economic Perspectives 20 (1), 153-174.
Suri, V., Chapman, D., 1998. Economic growth, trade and energy implications for the environmental Kuznets curve. Ecological Economics 25, 195-208.
Tatom, John, A. 2001. Review of: financial volatility and real economic activity. Journal of Economic Literature 39 (3), 917-918.
Taskin, F., Zaim, O., 2000. Searching for a Kuznets curve in environmental efficiency using kernel estimation. Economics Letters 68, 217-233.
Tsang, R., 1999. Open outcry and electronic trading in futures exchanges. Bank of Canada Review Spring, 21-39.
Weiner, R.J., 2002. Sheep in wolves’ clothing? speculators and price volatility in petroleum futures. The Quarterly Review of Economics and Finance 42 (2), 391-400.
Wirl, F., Kujundzic, A., 2004. The impact of OPEC conference outcomes on world oil price 1984-2001. The Energy Journal 25 (1), 45-62.
Zhao, X., Wu. Y., 2007. Determinants of China’s energy imports: an empirical analysis. Energy Policy 35, 4235-4246.
Zhou, F., 1999. Role of gas in China’s energy economic and long-term forecast for natural gas demand. Paper presented at the Sino-IEA Conference on Natural Gas Industry, Beijing, November 9-10, 1999.
Zou, G., Chau, K., 2006. Short- and long-run effects between oil consumption and economic growth in China. Energy Policy 34, 3644-3655.
黃宗煌和鄧秀玲(2005) “環境顧志耐曲線的斜率以及關稅的環境效果” Working paper.

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