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題名 考慮狀態轉換下的GARCH模型配適程度與預測能力之驗證 －以道瓊歐洲石油天然氣指數期貨為例
GARCH models under Regime Switching - DJ EURO STOXX OIL & GAS Index Futures
作者 張庭瑋
貢獻者 杜化宇
張庭瑋
關鍵詞 GARCH
Regime Switching
狀態轉換
指數期貨
共同基金
日期 2009
上傳時間 9-Sep-2013 11:28:36 (UTC+8)
摘要 　　本篇論文主要在檢視Fong與See (2001) 所提出的假說，將其應用於道瓊歐洲石油天然氣指數期貨 (DJ EURO STOXX OIL & GAS Index Futures) 上，是否能得到相同的驗證。

　　在是否加入狀態轉換考量的檢定中，本文採用AIC與BIC準則為判斷的基準，而由於雙狀態下BIC準則易有樣本參數過大的懲罰特性，因此其中又以AIC為較佳判斷的準則。研究結果顯示，有考量狀態轉換的Regime Switching GARCH模型配適度會較無考量狀態轉換的GARCH模型為佳。而在納入狀態轉換的考量下，在Regime Switching GARCH模型及其相關衍生模型的比較中，主要是採用RS-GARCH(1,1)-N，RS-GARCH(1,1)-t以及RS-ARCH(1,1)-t模型作為比較。這裡同樣以AIC與BIC準則為判斷的基準，研究結果顯示，在三模型中，是以RS-GARCH(1,1)-t模型具有最佳的配適度。

　　在預測能力的檢定中，本研究是利用MSE、MAE與R2，來判斷何者具有較佳的解釋能力，並且以DM檢定來進一步驗證。研究結果顯示，在有考量狀態轉換的Regime Switching GARCH模型與無考量狀態轉換的GARCH模型中，是以有考量狀態轉換的Regime Switching GARCH模型具有較佳的預測能力；而在RS-GARCH(1,1)-N，RS-GARCH(1,1)-t以及RS-ARCH(1,1)-t三種衍生模型的比較中，又以同時考量t分配以及有狀態轉換的RS-GARCH(1,1)-t模型具有較佳的預測能力。
參考文獻 1. Adrian R. Pagan and G. William Schwert (1990), “Alternative Models For Conditional Stock Volatility”, Journal of Econometrics, Vol.45, P.267-290.

2. Akgiray (1989), “Conditional heteroskedasticity in time series of stock returns: Evidence and forecasts”, Journal of Business, Vol.62，P.55~80

3. Alizadeh, Amir H., Nikos K. Nomikos and Panos K. Pouliasis (2008), “A Markov regime switching approach for hedging energy commodities”, Journal of Banking & Finance, Vol.32, Issue 9, P.1970-1983.

4. Baillie, R. T., and R. P. DeGennaro (1990), “Stock Returns and Volatility,” Journal of Finance and Quantitative analysis, Vol.25, P.203-214.

5. Bollerslev, T. (1986), “Generalized Autoregressive Conditional Heteroscedasticity”, Journal of Econometrics, Vol.31, P.307-27.

6. Bollerslev, T., R. Y. Chou, and K. F. Kroner (1992), “ARCH Modeling in Finance: A Review of the Theory and Empirical Evidence”, Journal of Econometrics, Vol.52, P.5-59.

7. Canarella, Giorgio and Stephen K. Pollard (2007), “A switching ARCH (SWARCH) model of stock market volatility：some evidence from Latin America”, International Review of Economics, Vol.54, Issue 12, P.445-462.

8. Chou, R. F. (1988), “Volatility Persistence and Stock Valuations: Some Empirical Evidence Using GARCH”, Journal of Applied Econometrics, Vol.3, P.279-294.

9. Dickey, D.A. and W.A. Fuller (1981), “Likelihood Ratio Statistic for Autoregressive Time Series with a Unit Root”, Econometrica, Vol.49, P.143-159.

10. Duker, M. J. (1997), “Markov Switching in GARCH Processes and Mean-Reverting Stock-Market Volatility”, Journal of Business and Economic Statistics 15(1), P.26-34.

11. Engle, R. (1982), “Autoregressive Conditional Heteroscedasticity with Estimates of Variance of UK Inflation”, Econometrica, Vol. 50, P.987-1008.


12. Engle, R. and T. Bollerslev (1986), “Modeling the Persistence of Conditional Variance”, Econometric Reviews, Vol. 5, P.1-50.

13. Fong, Wai Mun and Kim Hock See (2001), “Modelling the Conditional Volatility of Commodity Index Futures as a Regime Switching Process”, Journal of Applied Econometrics, Vol.16, Issue 2, P.133-163.

14. Fong, Wai Mun and Kim Hock See (2002), “A Markov switching model of the conditional volatility of crude oil futures prices”, Energy Economics, Vol.24, Issue 1, P.71-95.

15. Gosset , William Sealy (1908), “The probable error of a mean”, Biometrika 6 (1), Mar, P.1–25.

16. Gray, S. F. (1996), “Modeling the conditional distribution of interest rates as a regime-switching process”, Journal of Financial Economics, Vol.42, P.27- 62.

17. Hamilton, J. D. and R. Susmel. (1994), “Autoregressive Conditional Heteroskedasticity and Changes in Regime”, Journal of Econometrics, Vol.64, Issue 1-2, P.307-333.

18. Hansen, B. E. (1992), “The Likelihood Ratio Test Under Nonstandard Conditions: Testing the Markov Switching Model of GNP”, Journal of Applied Econometrics, Vol. 7, Supplement: Special Issue on Nonlinear Dynamics and Econometrics (Dec., 1992), p.S61-S82

19. Lee, Hsiang-Tai, Jonathan K. Yoder, Ron C. Mittelhammer and Jill J. McCluskey (2005), “A random coefficient autoregressive Markov regime switching model for dynamic futures hedging”, Journal of Futures Markets, Vol.26, Issue 2, P.103-129.

20. Li, Ming-Yuan Leon and Hsiou-Wei William Lin (2003) “Examining the Volatility of Taiwan Stock Index Returns via a Three-Volatility-Regime Markov-Switching ARCH Model”, Review of Quantitative Finance and Accounting, Vol.21, Issue 2 P.123-39.

21. Klaassen, Franc (2001), “Improving GARCH Volatility Forecasts with Regime-Switching GARCH”, Empirical Economics, Vol.27, Issue 2, P. 363-394.

22. Marcucci, J. (2005), “Forecasting Stock Market Volatility with Regime-switching GARCH Models”, Studies in Nonlinear Dynamics & Econometrics, Vol.9, P.1-53.

23. Poon, S. H. and Granger, C.W.J. (2003), “Forecasting Volatility in Financial Markets: A Review,” Journal of Futures Markets, Vol. 16:4, P.353-387.

24. Schwert, G. W. (1989), “Why Does Stock Market Volatility Change Over Time?” Journal of Finance, Vol.44, P.1115-1153.

25. EUREX：http://www.eurexchange.com/
描述 碩士
國立政治大學
財務管理研究所
96357035
98
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0096357035
資料類型 thesis
dc.contributor.advisor 杜化宇zh_TW
dc.contributor.author (Authors) 張庭瑋zh_TW
dc.creator (作者) 張庭瑋zh_TW
dc.date (日期) 2009en_US
dc.date.accessioned 9-Sep-2013 11:28:36 (UTC+8)-
dc.date.available 9-Sep-2013 11:28:36 (UTC+8)-
dc.date.issued (上傳時間) 9-Sep-2013 11:28:36 (UTC+8)-
dc.identifier (Other Identifiers) G0096357035en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/60630-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 財務管理研究所zh_TW
dc.description (描述) 96357035zh_TW
dc.description (描述) 98zh_TW
dc.description.abstract (摘要) 　　本篇論文主要在檢視Fong與See (2001) 所提出的假說，將其應用於道瓊歐洲石油天然氣指數期貨 (DJ EURO STOXX OIL & GAS Index Futures) 上，是否能得到相同的驗證。

　　在是否加入狀態轉換考量的檢定中，本文採用AIC與BIC準則為判斷的基準，而由於雙狀態下BIC準則易有樣本參數過大的懲罰特性，因此其中又以AIC為較佳判斷的準則。研究結果顯示，有考量狀態轉換的Regime Switching GARCH模型配適度會較無考量狀態轉換的GARCH模型為佳。而在納入狀態轉換的考量下，在Regime Switching GARCH模型及其相關衍生模型的比較中，主要是採用RS-GARCH(1,1)-N，RS-GARCH(1,1)-t以及RS-ARCH(1,1)-t模型作為比較。這裡同樣以AIC與BIC準則為判斷的基準，研究結果顯示，在三模型中，是以RS-GARCH(1,1)-t模型具有最佳的配適度。

　　在預測能力的檢定中，本研究是利用MSE、MAE與R2，來判斷何者具有較佳的解釋能力，並且以DM檢定來進一步驗證。研究結果顯示，在有考量狀態轉換的Regime Switching GARCH模型與無考量狀態轉換的GARCH模型中，是以有考量狀態轉換的Regime Switching GARCH模型具有較佳的預測能力；而在RS-GARCH(1,1)-N，RS-GARCH(1,1)-t以及RS-ARCH(1,1)-t三種衍生模型的比較中，又以同時考量t分配以及有狀態轉換的RS-GARCH(1,1)-t模型具有較佳的預測能力。		zh_TW
dc.description.tableofcontents 第壹章　緒論 4
第一節　研究背景 5
第二節　研究動機 7
第三節　研究問題與目的 9
第四節　論文架構以及研究流程 10
第貳章　文獻回顧 12
第一節　Regime Switching GARCH模型 12
第二節　Hedging 16
第參章　研究方法 17
第一節　研究標的 17
第二節　模型設定 21
第三節　模型檢定方法 25
第四節　檢定流程 28
第肆章　實證分析 29
第一節　基本資料驗證 29
第二節　模型檢定 33
第三節　模型比較 34
第四節　模型預測 37
第伍章　研究結論與建議 41
附錄 43
參考文獻 44		zh_TW
dc.format.extent 475658 bytes-
dc.format.mimetype application/pdf-
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0096357035en_US
dc.subject (關鍵詞) GARCHzh_TW
dc.subject (關鍵詞) Regime Switchingzh_TW
dc.subject (關鍵詞) 狀態轉換zh_TW
dc.subject (關鍵詞) 指數期貨zh_TW
dc.subject (關鍵詞) 共同基金zh_TW
dc.title (題名) 考慮狀態轉換下的GARCH模型配適程度與預測能力之驗證 －以道瓊歐洲石油天然氣指數期貨為例zh_TW
dc.title (題名) GARCH models under Regime Switching - DJ EURO STOXX OIL & GAS Index Futuresen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) 1. Adrian R. Pagan and G. William Schwert (1990), “Alternative Models For Conditional Stock Volatility”, Journal of Econometrics, Vol.45, P.267-290.

2. Akgiray (1989), “Conditional heteroskedasticity in time series of stock returns: Evidence and forecasts”, Journal of Business, Vol.62，P.55~80

3. Alizadeh, Amir H., Nikos K. Nomikos and Panos K. Pouliasis (2008), “A Markov regime switching approach for hedging energy commodities”, Journal of Banking & Finance, Vol.32, Issue 9, P.1970-1983.

4. Baillie, R. T., and R. P. DeGennaro (1990), “Stock Returns and Volatility,” Journal of Finance and Quantitative analysis, Vol.25, P.203-214.

5. Bollerslev, T. (1986), “Generalized Autoregressive Conditional Heteroscedasticity”, Journal of Econometrics, Vol.31, P.307-27.

6. Bollerslev, T., R. Y. Chou, and K. F. Kroner (1992), “ARCH Modeling in Finance: A Review of the Theory and Empirical Evidence”, Journal of Econometrics, Vol.52, P.5-59.

7. Canarella, Giorgio and Stephen K. Pollard (2007), “A switching ARCH (SWARCH) model of stock market volatility：some evidence from Latin America”, International Review of Economics, Vol.54, Issue 12, P.445-462.

8. Chou, R. F. (1988), “Volatility Persistence and Stock Valuations: Some Empirical Evidence Using GARCH”, Journal of Applied Econometrics, Vol.3, P.279-294.

9. Dickey, D.A. and W.A. Fuller (1981), “Likelihood Ratio Statistic for Autoregressive Time Series with a Unit Root”, Econometrica, Vol.49, P.143-159.

10. Duker, M. J. (1997), “Markov Switching in GARCH Processes and Mean-Reverting Stock-Market Volatility”, Journal of Business and Economic Statistics 15(1), P.26-34.

11. Engle, R. (1982), “Autoregressive Conditional Heteroscedasticity with Estimates of Variance of UK Inflation”, Econometrica, Vol. 50, P.987-1008.


12. Engle, R. and T. Bollerslev (1986), “Modeling the Persistence of Conditional Variance”, Econometric Reviews, Vol. 5, P.1-50.

13. Fong, Wai Mun and Kim Hock See (2001), “Modelling the Conditional Volatility of Commodity Index Futures as a Regime Switching Process”, Journal of Applied Econometrics, Vol.16, Issue 2, P.133-163.

14. Fong, Wai Mun and Kim Hock See (2002), “A Markov switching model of the conditional volatility of crude oil futures prices”, Energy Economics, Vol.24, Issue 1, P.71-95.

15. Gosset , William Sealy (1908), “The probable error of a mean”, Biometrika 6 (1), Mar, P.1–25.

16. Gray, S. F. (1996), “Modeling the conditional distribution of interest rates as a regime-switching process”, Journal of Financial Economics, Vol.42, P.27- 62.

17. Hamilton, J. D. and R. Susmel. (1994), “Autoregressive Conditional Heteroskedasticity and Changes in Regime”, Journal of Econometrics, Vol.64, Issue 1-2, P.307-333.

18. Hansen, B. E. (1992), “The Likelihood Ratio Test Under Nonstandard Conditions: Testing the Markov Switching Model of GNP”, Journal of Applied Econometrics, Vol. 7, Supplement: Special Issue on Nonlinear Dynamics and Econometrics (Dec., 1992), p.S61-S82

19. Lee, Hsiang-Tai, Jonathan K. Yoder, Ron C. Mittelhammer and Jill J. McCluskey (2005), “A random coefficient autoregressive Markov regime switching model for dynamic futures hedging”, Journal of Futures Markets, Vol.26, Issue 2, P.103-129.

20. Li, Ming-Yuan Leon and Hsiou-Wei William Lin (2003) “Examining the Volatility of Taiwan Stock Index Returns via a Three-Volatility-Regime Markov-Switching ARCH Model”, Review of Quantitative Finance and Accounting, Vol.21, Issue 2 P.123-39.

21. Klaassen, Franc (2001), “Improving GARCH Volatility Forecasts with Regime-Switching GARCH”, Empirical Economics, Vol.27, Issue 2, P. 363-394.

22. Marcucci, J. (2005), “Forecasting Stock Market Volatility with Regime-switching GARCH Models”, Studies in Nonlinear Dynamics & Econometrics, Vol.9, P.1-53.

23. Poon, S. H. and Granger, C.W.J. (2003), “Forecasting Volatility in Financial Markets: A Review,” Journal of Futures Markets, Vol. 16:4, P.353-387.

24. Schwert, G. W. (1989), “Why Does Stock Market Volatility Change Over Time?” Journal of Finance, Vol.44, P.1115-1153.

25. EUREX：http://www.eurexchange.com/		zh_TW