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研究生:楊雅瑜
研究生(外文):yang ya yu
論文名稱:非線性時間數列模式之比較分析-具結構性改變之原油價格資料預測
論文名稱(外文):Forecasting Performance of Nonlinear Time Series Models—An Application of Crude Oil Prices with Structural Changes
指導教授:許玉雪許玉雪引用關係
指導教授(外文):HSU ESHER
學位類別:碩士
校院名稱:國立臺北大學
系所名稱:統計學系
學門:數學及統計學門
學類:統計學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:中文
論文頁數:60
中文關鍵詞:ARFIMAARCHFIGARCHMS(Markov Switching )結構性變動石油價格價格預測RMSE。
外文關鍵詞:ARFIMAARCHFIGARCHMarkov Switching ModelRegime-switchingForecasting of Oil prices
相關次數:
  • 被引用被引用:7
  • 點閱點閱:281
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:1
1991 年波灣戰爭以來,油價波動極巨,直接衝擊經濟發展,石油價格的變化就成了經濟預測的重要指標,及早預知石油價格的走勢有助於經濟上因應對策之擬定。近幾年油價飆升的趨勢,隱含未來石油價格的結構性改變,而過去探討石油價格結構性改變的研究不多。因此,本研究之目的乃在於比較分析用於具有結構性改變之時間數列資料的非線性時間數列模式,ARFIMA、ARCH、FIGARCH、MS 數種模式之預測能力,以 1973到 2006 年的原油價格來進行分析,研究方法包含:(1)實證分析及(2)蒙地卡羅 ( Monte Carlo ) 模擬。
實證分析方面,係運用上述幾種非線性時間數列模式配置石油價格,進行最大概似的參數估計,並以樣本內資料來檢測模式的適合度及樣本外資料評估各種預測模式的預測能力,均方根差 ( root mean-square error;RMSE ) 為本文主要的預測能力評估準則,實證結果用以比較上述模型的預測能力,並從中選取最佳的預測模型做為預測未來原油價格的依據。模擬分析方面則根據實証分析結果加入結構性改變的假設,找出具有結構改變的原油價格可能模式,分別為 MS(2)、平均數不等變異數相等之MS(2)-AR(2)模式、和平均數相等變異數不等之MS(2)-AR(2)模式、ARFIMA(1,0)和ARCH(2),並據以模擬具有結構性改變的石油價格資料,透過ARFIMA、ARCH、FIGARCH、MS數種模式進行配適,模擬評估前述數種模式的配適能力。實證結果以MS(2)-AR(2)預測能力最佳;模擬結果發現當石油價格具有明顯的結構改變時,長記憶ARFIMA模式比MS模式的預測能力差;而若結構的變動不大或持續不長久時,MS模式預測能力不比ARFIMA好。
Crude oil price has fluctuated very huge after Gulf War in 1999 which has significant impact on economic development globally. Oil price forecasting, therefore, becomes an important issue for helping decision-making of economic policy. This paper aims to compare the forecasting performance of nonlinear time series models, ARFIMA, ARCH, FIGARCH and MS models, for time series data with structural changes. Empirical analysis and Monte Carlo experiment are employed in this paper.
Empirical analysis is used to model monthly crude oil prices in the period 1973-2006 and evaluate the forecast performance. In response to the expected structural changes of future oil prices, based upon the estimated model from empirical results, five data generation process, namely (1) simple two regimes model, (2) two regimes model with mean is unequity and variance is equity, (3) two regimes model with mean is equity and variance is unquity, (4) fractionally integrated ARMA model with first-order autoregression, and (5) ARCH model with second-order autoregression are used to generate time series data of oil price with structural changes. The forecasting performance of ARFIMA, ARCH, FIGARCH, and MS models are further evaluated.
The empirical results show that forecasting performance of MS(2)-AR(2) is better than others. The simulation results show that forecasting performance of MS model is better than long memory ARFIMA model as oil prices have significantly structural changes. For the series without significantly structural changes and less persistent regimes, forecasting performance of ARFIMA model is better than MS model.
目 錄

壹 緒論 1
1.1 研究動機與目的 1
1.2 研究資料 2
1.3 研究架構 3
貳 文獻回顧 5
2.1 方法論相關文獻回顧 5
2.2 石油價格相關文獻回顧 9
叁 石油價格分析 12
肆 研究方法 14
4.1 ARFIMA 模式 14
4.2 ARCH 模式 19
4.3 FIGARCH 模式 22
4.4 MARKOV SWITCHING MODEL (MS) 23
伍 實證分析 28
陸 模擬分析 35
6.1 DGP為MS(2) 36
6.2 DGP為MS(2)-AR(2) 37
6.3 DGP為ARFIMA(1,D,0) 40
6.4 DGP為ARCH(2) 42
柒 結論與建議 44
7.1研究結論 44
7.2 未來研究方向與建議 45
參考文獻 47
附錄 50
一、原油價格月資料表 50
二、實証石油價格各個模式之配適圖 53
三、實証石油價格各個模式之樣本外預測 58
四、實証石油價格各個模式之樣本內外預測 59
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