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研究生:周秋如
研究生(外文):Chiu-Ru Chou
論文名稱:原油期貨報酬條件波動度預測績效:對稱與不對稱條件波動模型之比較
論文名稱(外文):Comparative Forecasting Performance of Symmetric and Asymmetric Conditional Volatility Models of a Crude Oil Futures Returns.
指導教授:孫鈺峰孫鈺峰引用關係
指導教授(外文):Yu-Fong Sun
學位類別:碩士
校院名稱:嶺東科技大學
系所名稱:財務金融研究所
學門:商業及管理學門
學類:財務金融學類
論文種類:學術論文
畢業學年度:98
語文別:中文
論文頁數:37
中文關鍵詞:不對稱條件波動預測績效槓桿效果
外文關鍵詞:Asymmetric conditional volatilityForecasting performanceleverage effect
相關次數:
  • 被引用被引用:5
  • 點閱點閱:512
  • 評分評分:
  • 下載下載:111
  • 收藏至我的研究室書目清單書目收藏:2
本文針對2000年1月1日至2010年3月17日原油期貨報酬進行波動預測,採用的研究方法是使用GARCH、TGARCH及EGARCH模型進行估計預測,藉此比較對稱與不對稱性波動模型之間對波動預測績效的差異。
本文加入虛擬變數避免來自兩房危機所引發的全球金融風暴產生結構性改變所造成的偏誤。實證結果顯示原油期貨市場存在波動不對稱性(槓桿效果),導致TGARCH模型和EGARCH模型的預測績效皆優於GARCH模型,因此波動不對稱性是否存在應成為預測波動重要因素。
在TGARCH模型和EGARCH模型中顯示:全球性的金融風暴不僅使原油期貨報酬下跌,也加大波動風險,而GARCH模型無法正確顯示全球性的金融風暴對波動風險的影響。
In this paper, we use GARCH, TGARCH and the EGARCH model to forecast the volatility of crude oil futures returns from January 1, 2000 to March 17, 2010, compared to symmetric and asymmetric volatility on volatility forecasting performance between models difference.
This article by adding dummy variables to avoid bias from Fannie Mae and Freddie Mac debt induced structural changes in financial crisis. The empirical results show the existence of crude oil futures market asymmetric volatility (leverage effect), resulting in TGARCH model and the EGARCH model is better than the forecast performance of GARCH models, therefore the existence of asymmetric volatility should be the important factor in forecasting volatility.
TGARCH model and the EGARCH models show that global financial turmoil has not only paid down crude oil futures, but also increase volatility, and GARCH model can not correctly display the global financial turmoil on the volatility risk.
中文摘要 i
英文摘要 ii
誌謝 iii
目錄 iv
表目錄 vi
圖目錄 vii
第一章 緒論 1
第一節 研究動機與目的 1
第二節 研究流程 4
第二章 文獻回顧 5
第三章 研究方法 8
第一節 ADF單根檢定 8
第二節 ARCH檢定 9
第三節 GARCH模型 11
第四節 條件變異數不對稱檢定 13
第五節 TGARCH模型與EGARCH模型 16
第六節 預測表現評估 18
第四章 實證結果與分析 21
第一節 ADF單根檢定 21
第二節 ARCH檢定 24
第三節 GARCH(1,1)模型實證結果 25
第四節 條件變異數不對稱檢定 26
第五節 TGARCH模型與EGARCH模型實證結果 27
第六節 預測表現評估實證結果 30
第五章 結論與建議 32
第一節 結論 32
第二節 建議 34
參考文獻 35
一、中文部分
1. 王甡(1995),「報酬衝擊對條件波動所造成之不對稱效果-台灣股票市場之實證分析」,證券市場發展季刊,第七卷,第一期,125-160
2. 王雅瑜(2008),「油價坡動對美國與日本股價市場報酬之衝擊:雙變量厚尾分配與DCC非對稱IGARCH模型之應用」,嶺東科技大學碩士論文。
3. 洪菁穗(2008),「影響石油需求的不對稱性」,東吳大學碩士論文。
4. 郭博堯(2003),「全球石油危機對油價的衝擊」,國家政策論壇季刊,夏季號。
5. 陳旭昇(2009),「時間序列分析-總體經濟與財務金融之應用」,東華書局。
6. 陳維邦(2008),「股價與石油價格波動性之關係-動態條件相關多變量模型之應用」,逢甲大學碩士論文。
7. 劉洪鈞(2006),「厚尾 GARCH 模型之波動性預測績效」,淡江大學博士論文。
8. 謝欣穎(2008),「原油期貨與股價指數波動度領先落後關係之研究」,中華大學碩士論文。
二、英文部分
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2. Akgiray, V. (1989), “Conditional Heteroskedasticity in Time Series of Stock Return: Evidence and Forecasts,” Journal of Business, 62, 55-80.
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6. Bollerslev, T. (1987), “A Conditional Heteroskedasticity Time Series Model for Speculative,” Review of Economic and Statistics 69, 542-547.
7. Campell, J. and Hentschel, L. (1992), “No News is Good News :An Asymmetric Model of Changing Volatility in Stock Returns.” Journal of Financial Economic 31, 281-318.
8. Christie, A. (1982), “The Stochastic Behavior of Common Stock Variance:Value, Leverage and Interest Rate Effects,” Journal of Financial Economics 10, 407-432.
9. Chu, Shin-Herng and Freund, S. (1996), “Volatility Estimation for Stock Index Options: A GARCH Approach,” Quarterly Review of Economics and Finance 36, 431-450.
10. Dickey, D. A. and Fuller, W. A. (1981), “Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root,” Econometrica, 49, 1057-1072.
11. Diebold, F. X. and Mariano, R. S. (1995), “Comparing Predictived Accuracy,” Journal of Business and Economic Statistics 13, 253-263
12. Engle, R. F. (1982), “Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of UK Inflation,” Econometrica 50, 987-1008.
13. Engle, R. (2002), “Dynamic Conditional Correlation-A simple class of multivariate GARCH models,” Journal of Business and Economic Statistics, Vol.20, No.3, 339-350.
14. Engle, R. F. and Bollerslev, T. (1986), “Modeling the Persistence of Conditional Variance,” Econometric Review 35, 1-50
15. Engle, R. F. and Ng, V. (1993), “Measuring and Testing the Impact of News on Volatility,” Journal of Finance 45, 1749-1777.
16. Engle, R. F., V. Ng and Rothschild, M. (1990), “Asset Pricing with a Factor ARCH Covariance Structure: Empirical Estimates for Treasury Bills,” Journal of Econometrics 45, 213-38.
17. Engle, R.F. and Yoo, B.S. (1987), “Forecasting and Testing in Cointegrated Systems,” Journal of Econometrics 35, 143-159.
18. Gately, Dermot and Huntington, H. G. (2002), “The Asymmetric Effects of Changes in Price and Income on Energy and Oil Demand.” The Energy Journal 23(1): 19-55
19. Glosten, L., Jagannathan, R. and Runkle, D. (1993), “On the Relation Between the Expected Value and the Volatility on the Nominal Excess Returns on Stocks,” Journal of Finance 48, 1779-1801.
20. Granger, C.W. J. (1969), “Investigation Causal Relations by Econometric Models and Cross-Ppectral Methods,” Econometrica 37, 424-438.
21. Hentschel, L. (1995), “All in the Family Nesting Symmetric and Asymmetric GARCH Models,” Journal of Financial Economics 39, 71-104.
22. Lobo, B. J. nad Tufte, D. (1998), “Exchange Rate Volatility:Does Politics Matter?” Journal of Macroeconomics 20, 351-365.
23. Nelson, D. B. (1991), “Conditional Heteroscedasticity in Asset Returns:A new Approach,” Econometrica 59, 347-370.
24. Newey, W. K. and West, K. D. (1987), “A Simple, Positive Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix,” Econometrica 55, 703-708.
25. Schwarz, G. (1990), “Stock Volatility and the Crash of 87,” Review of Financial and Studies 3, 77-102.
26. Schwarz, G., (1978). “Estimating the Dimension of a Model,” Annals of Statistics 6, 461-464.
27. Zakoian, J. M. (1994), “Threshold Heteroskedastic Models,” Journal of Economic Dynamics and Control 18, 931-955.
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