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題名 黃金價格預測探討-跳躍模型之改良
On Forecasting Gold Price: An Improved Jump and Dip Forecasting Model
作者 方玠人
Fang, Chieh Jen
貢獻者 蕭又新<br>郭訓志
方玠人
Fang, Chieh Jen
關鍵詞 黃金價格
單根檢定
跳躍模型
Gold Price
Unit Root Test
Jump and Dip Model
日期 2011
上傳時間 1-Nov-2012 13:58:44 (UTC+8)
摘要 本文改良了Shafiee-Topal(2010)所提出之跳躍模型之波動率，並歸納成三種模型:改良跳躍模型、改良平滑跳躍模型以及最佳化跳躍模型，並運用時間序列模型探討樣本期間內黃金價格。第一部份比較三種跳躍模型與Shafiee-Topal模型在訓練集及測試集的預測結果，並預測2012年至2018年之黃金價格走勢。第二部份探討黃金價格、原油價格以及美元加權指數之間的互動關係，建立多變數模型以預測黃金價格之長期趨勢。
首先，本文檢驗黃金價格、原油價格及美元加權指數樣本之恆定性，經由ADF 單根檢定法發現序列具有單根，進而使用TSP(Trend Stationary Process)估計模型參數。其次，黃金價格、原油價格及美元加權指數經共整合檢定發現，各模型變數間均具有共整合關係，即變數間具有長期均衡關係。黃金價格與原油價格呈正向反應，而黃金價格和原油價格與美元加權指數呈負向反應，除了受自身的預測解釋能力外，亦可以做為觀察其他變數的未來走勢方向及影響大小預估。最後，探討黃金價格受波動率的影響情形，本文改良Shafiee-Topal模型之波動率，並比較四種模型對黃金價格趨勢預測之結果，發現改良平滑跳躍模型在實際黃金價格波動率大時，其趨勢預測結果會優於Shafiee-Topal模型。
This research advanced the volatility component (λ) of the jump and dip model (Shafiee and Topal,2010) on gold prices from 1968 to 2012 and estimated the gold price for the next 6 years. Based on the trend stationary process, we defined the three components and derived three new models: Adjusted Jump and Dip Model, Adjusted Smooth Jump and Dip Model and Optimized Jump and Dip Model.
First part of the thesis compared the performance in prediction of the training data and the testing data for three different models and the jump and dip model. Second part of the thesis investigated the relationship among the gold price, crude oil price, and trade weighted U.S. dollar index of the concepts The result illustrated the long term trend of gold price described by a multivariate predictive model. We found evidence that different levels of volatility affect the prediction of gold price, and the adjusted jump and dip Model performs best when the true volatility is relatively high.
參考文獻 西文部份:
Anderson, O. D. (1980). Analysing Time Series: . Amsterdam: North-Holland.
Baffes, J. (2007). Oil Spills on Other Commodities. Resources Policy, 32,
126-134.
Banerjee, A., Dolado, J., Galbraith, J. W., & Hendry, D. (1993).
Co-integration, Error Correction, and the Econometric Analysis of
Non-Stationary Data. USA: Oxford University Press.
Box, G. E. P., & Jenkins, G. M. (1994). Time Series Analysis: Forecasting
and Control (3rd ed.): Prentice Hall.
Brown, R. G., Meyer, R. F., & D`Esopo, D. A. (1961). The Fundamental Theorem
of Exponential Smoothing. Operations Research, 9(5), 673-687.
Chatfield, C. (2003). The Analysis of Time Series: An Introduction (6th ed.).
New York: Chapman and Hall/CRC.
Dickey, D. A., & Fuller, W. A. (1979). Distribution of the Estimators for
Autoregressive Time Series With a Unit Root. Journal of the Ameican
Statistical Association, 74(366), 427-431.
Engle, R. F., & Granger, C. W. J. (1987). Co-Integration and Error Correction:
Representation, Estimation, and Testing. Econometrica, 55(2), 251-276.
Granger, C. W. J., & Newbold, P. (1974). Supirous Regression in economics.
Journal of Econometrics, 2, 111-120.
Greenberg, E., & Webster, C. E. (1983). Advanced Econometrics: A Bridge to
the Literature. New York: John Wiley and Sons.
Maddala, G. S., & Kim, I.-M. (1998). Unit Roots, Cointegration, and
Structural Change. Cambridge: Cambridge University Press.
Nelson, C. R., & Plosser, C. I. (1982). Trends and random walks in
macroeconmic time series. Some evidence and implications. Journal of
Monetary Economics, 10, 139-162.
Pankratz, A. (1983). Forecasting with Univariate Box-Jenkins Models Concepts
and Cases. New York: John Wiley and Sons.
Phillips, P. C. B., & Ouliaris, S. (1990). Asymptotic Properties of Residual
Based Tests for Cointegration. Econometrica, 58(1), 165-193.
Pindyck, R. S., & Rotemberg, J. J. (1990). The Excess Co-Movement of Commodity
Prices. The Economic Journal, 100(403), 1173-1189.
Said, S. E., & Dickey, D. A. (1984). Testing for unit roots in
autoregressive-moving average models of unknown order. Biometrika,
71(3), 599-607.
Shafiee, S., & Topal, E. (2010). An overview of global gold market and gold
price forecasting. Resources Policy, 35(3), 178-189.

中文部份:
楊奕農(2011)。時間序列分析-經濟與財務上之應用 (二版)。 台北: 雙葉書廊。
描述 碩士
國立政治大學
應用物理研究所
98755013
100
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0098755013
資料類型 thesis
dc.contributor.advisor 蕭又新<br>郭訓志zh_TW
dc.contributor.author (Authors) 方玠人zh_TW
dc.contributor.author (Authors) Fang, Chieh Jenen_US
dc.creator (作者) 方玠人zh_TW
dc.creator (作者) Fang, Chieh Jenen_US
dc.date (日期) 2011en_US
dc.date.accessioned 1-Nov-2012 13:58:44 (UTC+8)-
dc.date.available 1-Nov-2012 13:58:44 (UTC+8)-
dc.date.issued (上傳時間) 1-Nov-2012 13:58:44 (UTC+8)-
dc.identifier (Other Identifiers) G0098755013en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/55136-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 應用物理研究所zh_TW
dc.description (描述) 98755013zh_TW
dc.description (描述) 100zh_TW
dc.description.abstract (摘要) 本文改良了Shafiee-Topal(2010)所提出之跳躍模型之波動率，並歸納成三種模型:改良跳躍模型、改良平滑跳躍模型以及最佳化跳躍模型，並運用時間序列模型探討樣本期間內黃金價格。第一部份比較三種跳躍模型與Shafiee-Topal模型在訓練集及測試集的預測結果，並預測2012年至2018年之黃金價格走勢。第二部份探討黃金價格、原油價格以及美元加權指數之間的互動關係，建立多變數模型以預測黃金價格之長期趨勢。
首先，本文檢驗黃金價格、原油價格及美元加權指數樣本之恆定性，經由ADF 單根檢定法發現序列具有單根，進而使用TSP(Trend Stationary Process)估計模型參數。其次，黃金價格、原油價格及美元加權指數經共整合檢定發現，各模型變數間均具有共整合關係，即變數間具有長期均衡關係。黃金價格與原油價格呈正向反應，而黃金價格和原油價格與美元加權指數呈負向反應，除了受自身的預測解釋能力外，亦可以做為觀察其他變數的未來走勢方向及影響大小預估。最後，探討黃金價格受波動率的影響情形，本文改良Shafiee-Topal模型之波動率，並比較四種模型對黃金價格趨勢預測之結果，發現改良平滑跳躍模型在實際黃金價格波動率大時，其趨勢預測結果會優於Shafiee-Topal模型。		zh_TW
dc.description.abstract (摘要) This research advanced the volatility component (λ) of the jump and dip model (Shafiee and Topal,2010) on gold prices from 1968 to 2012 and estimated the gold price for the next 6 years. Based on the trend stationary process, we defined the three components and derived three new models: Adjusted Jump and Dip Model, Adjusted Smooth Jump and Dip Model and Optimized Jump and Dip Model.
First part of the thesis compared the performance in prediction of the training data and the testing data for three different models and the jump and dip model. Second part of the thesis investigated the relationship among the gold price, crude oil price, and trade weighted U.S. dollar index of the concepts The result illustrated the long term trend of gold price described by a multivariate predictive model. We found evidence that different levels of volatility affect the prediction of gold price, and the adjusted jump and dip Model performs best when the true volatility is relatively high.		en_US
dc.description.tableofcontents 第 1 章 緒論..................................................... 1
1.1 研究動機與目的........................................... 1
第 2 章 文獻探討................................................. 6
第 3 章 研究方法................................................ 10
3.1 時間序列分析............................................ 10
3.1.1 自迴歸移動平均模型(ARIMA) ........................... 10
3.1.2 定態(Stationary) .................................... 11
3.2 單根檢定(Unit Root Test)................................ 13
3.2.1 Dickey-Fuller 檢定 ................................... 14
3.2.2 Augmented Dickey-Fuller 檢定 ........................ 16
3.3 共整合分析(Co-integration).............................. 18
3.3.1 Engle-Granger 共整合檢定 ............................. 19
3.4 指數平滑法（Exponential Smoothing）..................... 20
3.5 研究流程................................................ 21
第 4 章 資料描述................................................ 24
4.1 黃金價格................................................ 24
4.2 原油.................................................... 26
4.3 美元加權指數............................................ 28
第 5 章 實證結果與建議.......................................... 30
5.1 相關係數分析............................................ 30
5.2 單根檢定結果............................................ 30
5.3 樣本外預測力評估........................................ 32
5.4 跳躍模型之波動率改良.................................... 32
5.4.1 資料期間:1968 年-1998 年、預測期間:1999 年-2008 年 ........ 33
5.4.2 資料期間:1968 年-2008 年、預測期間:2009 年-2012 年5 月 ..... 42
5.4.3 資料期間:1968 年-2012 年5 月、預測期間: 2012 年5 月-2018 年 47
5.5 多變數模型.............................................. 53
5.6 研究建議................................................ 57
附錄.............................................................. 58
附錄一:1968 年-1998 年黃金價格趨勢線之殘差值、RMSE 及MAE 值 .... 58
附錄二:1968 年-2008 年黃金價格趨勢線之殘差值、RMSE 及MAE 值 .... 64
附錄三:1968 年-2012 年黃金價格趨勢線之殘差值、RMSE 及MAE 值 .... 70
附錄四:1986 年-2012 年黃金價格趨勢線之殘差值、RMSE 及MAE 值 .... 75
附錄五:1986 年-2012 年多變數模型之殘差值、RMSE 及MAE 值 ........ 80		zh_TW
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0098755013en_US
dc.subject (關鍵詞) 黃金價格zh_TW
dc.subject (關鍵詞) 單根檢定zh_TW
dc.subject (關鍵詞) 跳躍模型zh_TW
dc.subject (關鍵詞) Gold Priceen_US
dc.subject (關鍵詞) Unit Root Testen_US
dc.subject (關鍵詞) Jump and Dip Modelen_US
dc.title (題名) 黃金價格預測探討-跳躍模型之改良zh_TW
dc.title (題名) On Forecasting Gold Price: An Improved Jump and Dip Forecasting Modelen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) 西文部份:
Anderson, O. D. (1980). Analysing Time Series: . Amsterdam: North-Holland.
Baffes, J. (2007). Oil Spills on Other Commodities. Resources Policy, 32,
126-134.
Banerjee, A., Dolado, J., Galbraith, J. W., & Hendry, D. (1993).
Co-integration, Error Correction, and the Econometric Analysis of
Non-Stationary Data. USA: Oxford University Press.
Box, G. E. P., & Jenkins, G. M. (1994). Time Series Analysis: Forecasting
and Control (3rd ed.): Prentice Hall.
Brown, R. G., Meyer, R. F., & D`Esopo, D. A. (1961). The Fundamental Theorem
of Exponential Smoothing. Operations Research, 9(5), 673-687.
Chatfield, C. (2003). The Analysis of Time Series: An Introduction (6th ed.).
New York: Chapman and Hall/CRC.
Dickey, D. A., & Fuller, W. A. (1979). Distribution of the Estimators for
Autoregressive Time Series With a Unit Root. Journal of the Ameican
Statistical Association, 74(366), 427-431.
Engle, R. F., & Granger, C. W. J. (1987). Co-Integration and Error Correction:
Representation, Estimation, and Testing. Econometrica, 55(2), 251-276.
Granger, C. W. J., & Newbold, P. (1974). Supirous Regression in economics.
Journal of Econometrics, 2, 111-120.
Greenberg, E., & Webster, C. E. (1983). Advanced Econometrics: A Bridge to
the Literature. New York: John Wiley and Sons.
Maddala, G. S., & Kim, I.-M. (1998). Unit Roots, Cointegration, and
Structural Change. Cambridge: Cambridge University Press.
Nelson, C. R., & Plosser, C. I. (1982). Trends and random walks in
macroeconmic time series. Some evidence and implications. Journal of
Monetary Economics, 10, 139-162.
Pankratz, A. (1983). Forecasting with Univariate Box-Jenkins Models Concepts
and Cases. New York: John Wiley and Sons.
Phillips, P. C. B., & Ouliaris, S. (1990). Asymptotic Properties of Residual
Based Tests for Cointegration. Econometrica, 58(1), 165-193.
Pindyck, R. S., & Rotemberg, J. J. (1990). The Excess Co-Movement of Commodity
Prices. The Economic Journal, 100(403), 1173-1189.
Said, S. E., & Dickey, D. A. (1984). Testing for unit roots in
autoregressive-moving average models of unknown order. Biometrika,
71(3), 599-607.
Shafiee, S., & Topal, E. (2010). An overview of global gold market and gold
price forecasting. Resources Policy, 35(3), 178-189.

中文部份:
楊奕農(2011)。時間序列分析-經濟與財務上之應用 (二版)。 台北: 雙葉書廊。		zh_TW