(3.80.6.131) 您好!臺灣時間:2021/05/15 01:36
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

: 
twitterline
研究生:林家仰
研究生(外文):Chia-Yang Lin
論文名稱:使用領先指標預測匯率變動方向之研究
論文名稱(外文):Predicting Exchange Rate Direction with Leading Indicators
指導教授:李富民李富民引用關係
指導教授(外文):Fu-Ming Lee
學位類別:碩士
校院名稱:朝陽科技大學
系所名稱:資訊管理系碩士班
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2005
畢業學年度:93
語文別:中文
論文頁數:116
中文關鍵詞:類神經網路灰關聯領先指標匯率預測
外文關鍵詞:Neural networkLeading indicatorsExchange rate forecastingGray relational analysis
相關次數:
  • 被引用被引用:8
  • 點閱點閱:596
  • 評分評分:
  • 下載下載:106
  • 收藏至我的研究室書目清單書目收藏:1
長期以來,由於匯率的波動將造成物價、股市、進出口…等等的變動,匯率預測一直是許多學者關注的議題。本論文嘗試預測短期(一、六和十二個月後)的匯率變動方向,以提供市場擇時操作(market timing)的資訊。本研究假設兩個國家間相對的總體經濟情況變化,將影響匯率的變動。而一個國家的領先指標,能適當地代表該國未來的總體經濟情況。透過類神經網路模型,本研究建構出兩國各自的領先指標和未來的匯率變動方向間關係。以四個國家(日、加、英、德)的貨幣相對美元的匯率進行實證預測,並和過去研究的測試期(1990~1997.7)結果相比較下,發現在六和十二個月後的預測上,正確性不止優於過去研究,也能夠顯著優於隨機漫步模型。
同時本研究導入灰關聯分析,期望找出較關鍵的領先指標,以增加預測的正確性並減少運算的複雜度。在大部分的實驗結果中,利用灰關聯選取的領先指標進行預測,能比未選取前更普遍的顯著於隨機漫步模型。但預測匯率變動方向的正確率,則未必較未選取前的表現好。
最後,本研究也利用相同的模型,對四個國家(日、加、英、台灣)貨幣近期(2000~2004.5)對美元的匯率變動進行預測。多數國家在一月和六月的預測能優於隨機漫步模型(除了加拿大),十二月後的預測有時反而正確率較低而且不顯著(日本、台灣)。另外,在此測試期中,不能驗證出進行灰關聯分析的選取的作法是較為有利。
根據實證的結果,本研究能比過去研究表現更佳,達到一定程度的正確率,並且顯著於隨機漫步模型,證明本研究的想法和模型有助於補足目前匯率研究不足之處,值得未來加強研究。
For a long time, prediction on exchange rate change has been a research issue, because the change of exchange rate will directly make commodity price, stock market, the import and export, etc. change. This paper attempts to predict exchange rate direction at the short horizon (1-, 6-, and 12-month) to provide the market-timing information. We presume that the relative variations of two countries’ macroeconomic conditions would therefore lead to exchange rate moving, and the leading indicators of a specific country could appropriately represent its future macroeconomic conditions. Neural network model is employed to describe the relationship between the leading indicators of two countries and the direction of their exchange rate in the future. To forecast four countries (Japan, Canada, U.K., Germany ) exchange rate and to compare the results by the test terms (1990~1997.7) of the past research, we find empirical evidence that our model outperforms the random walk model and appears to be more accurate at 6- and 12-month horizons than past work.
Gray relational analysis is also employed in hope to find out some key indicators. We expect the analysis can promote the prediction accuracy and reduce the operation complex. In greatly part of experiment results, using the indicators selecting by gray relational analysis to predict exchange rate direction can perform statically significantly superior to random walk. But the correct rates of forecasting the exchange rate direction not definitely perform better than unanalyzed.
Finally, this research also use the same model to predict four countries (Japan, Canada, U.K., Taiwan) to US dollar exchange rate direction in near term (2000~2000.5). The forecasting results in 1 and 6 month horizon can surpass random walk model in most countries, but sometimes the correct rates are lower and not significantly superior to random walk model in 12 month horizon. Besides, in this testing horizons, we can''t identify the method of the selection of gray relational analysis is more beneficial.
According to the empirical results, our work can perform statically significantly superior to random walk. The other is that the idea and model of this research can improve the insufficiency of current ones on exchange rate.
第一章、緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 論文架構 4
第二章、文獻探討 6
2.1 匯率預測 6
2.2 方向性預測 8
2.3 類神經網路 10
第三章、研究方法 13
3.1 研究假設 13
3.2 實驗架構 16
3.2.1 輸入變數---總體經濟領先指標 16
3.2.2 倒傳遞類神經網路 17
3.2.3 類神經網路架構 20
3.3 實驗數據來源和處理 23
3.4灰關聯 25
3.4.1 灰色理論 25
3.4.2 灰關聯處理 27
3.5 統計分析方法 29
3.5.1 方向正確率 (Directional Accuracy Ratio, DAR) 29
3.5.2 卡方獨立性檢定 29
3.5.3 PT檢定 31
第四章、實驗結果 34
4.1 與過去實驗比較 35
4.1.1 類神經網路模懝 35
4.1.1.1 日圓 35
4.1.1.2加拿大幣 39
4.1.1.3德國馬克 42
4.1.1.4 英鎊 45
4.1.1.5 小結 48
4.1.2 灰關聯對參數進行挑選 49
4.1.2.1 日圓 49
4.1.2.2 加拿大幣 53
4.1.2.3 德國馬克 57
4.1.2.4 英鎊 60
4.1.2.5 小結 63
4.2 近期研究 64
4.2.1 日圓 64
4.2.2 加拿大幣 68
4.2.3 英鎊 72
4.2.4 新台幣 76
4.2.5 小結 81
第五章、結論及未來研究方向 82
5.1 結論 82
5.2 未來研究方向 84
5.2.1 其他領先指標的選取 84
5.2.2 匯率的不穩定周期探討和調整 84
5.2.3 類神經網路架構改良 85
5.2.4 類神經網路和其他MODEL比較 86
參考文獻 87
附表一:實驗中所採用的各國領先指標 110
[1]施向陽(2001),匯率變動預測模式之研究,碩士論文,大葉大學,彰化。
[2]黃信棠(2004),理性預期、資料修正與名目匯率預測,碩士論文,國立中正大學,嘉義。
[3]翁慶昌,陳嘉欉,賴宏仁(2001),灰色系統基本方法及其應用,高立圖書有限公司,台北。
[4]葉怡成(2003),類神經網路模式應用與實作,儒林圖書有限公司。
[5]溫坤禮,黃宜豊,張偉哲,張廷政,游美利,賴家瑞(2003),灰關聯模型方法與應用,高立圖書有限公司,台北。
[6]劉瑞鑫(2003),時間序列與人工智慧方法在台股指數報酬率預測之績效比較,碩士論文,朝陽科技大學,臺中。
[7]鄧聚龍(1987),灰色系統基本方法,華中理工大學出版社,大陸。
[8]鄧聚龍(1999),灰色系統理論與應用,高立圖書有限公司,台北。
[9]J.C.K. Ash, D.J. Smyth, S.M. Heravi (1998), “Are OECD Forecasts Rational and Useful?: a Dictional Analysis,” International Journal of Forecasting, Vol. 14, pp. 381-391.
[10]P. Bustelo (2000), “Novelties of Financial Crises in the 1990s and the Search for new Indicators,” Emerging Markets Review, Vol. 1, pp. 229-251
[11]A.R. Barron (1991), “Universal Approximation Bounds for Superpositions of a Sigmoidal Function,” Technical Report, No. 58, Department of Statistics, University of Illinois, Urbana-Champaign.
[12]C. Brooks(1997), “Linear and Non-linear (Non-) Forecastability of High-frequency Exchange Rates,” Journal of Forecasting, Vol. 16, pp. 125-145.
[13]O. Burkart, V. Coudert (2002), “Leading Indicators of Currency Crises for Emerging Countries,” Emerging Markets Review, Vol. 3, pp. 107-133.
[14]M. Chauvet, S. Potter (2000), “Coincident and Leading Indicators of the Stock Market.” Journal of Empirical Finance, Vol. 7, pp. 87-111.
[15]A.S. Chen, M.T. Leung(2004), “Regression neural network for error correction in foreign exchange forecasting and trading”, Computer & Operations Research, Vol. 31, pp. 1049-1068.
[16]M. Chinn, R. Meese (1995), “Banking on Currency Forecasts: How Predictable is Change in Money?” Journal of International Economics, Vol. 38, pp. 161-178.
[17]M.P. Clements, D.F. Hendry (1998), Forecasting Economic Time Series, Cambridge: Cambridge University Press.
[18]S.P. Day, M.R. Davenport (1993), “Continuous-Time Temporal Back-Propagation with Adaptable Time Delays,” IEEE Transactions on Neural Networks, Vol. 4, pp.348-354.
[19]F.X. Diebold, R. Mariano (1995), ’’Comparing Predictive Accuracy’’, Journal of Business and Economic Statistics, Vol. 13, pp. 253-262.
[20]M. Duarte (2003), “Why don’t Macroeconomic Quantities Respond to Exchange Rate Variability?” Journal of Monetary economics, Vol. 50, pp. 889-913
[21]M.R. El Shazly, H.E. El Shazly (1999), “Forecasting Currency Prices Using a Genetically Evolved Neural Network Architecture,”International Review of Financial Analysis, Vol. 8, pp. 67-82.
[22]C. Engel(1994), “Can The Markov Switching Model Forecast Exchange Rae?”Journal of International Economics, Vol. 13. pp. 151-165.
[23]J. Faust, J.H. Rogers, J.H. Wright (2003), “Exchange Rate Forecasting: the Errors We’ve Really Made.” Journal of International Economics, Vol. 60, pp. 35-59.
[24]F. Ferna´ndez–Rodr´ıguez, S. Sosvilla–Rivero, J. Andrada–Fe´lix (1999), “Exchange-rate Forecasting with Simultaneous nearest-neighbour methods: evidence from the EMS,” International Journal of Forecasting, Vol. 15, pp. 383-392.
[25]J.M. Fleming (1962), “Domestic Financial Policies under Fixed and under Floating Exchange Rates,” Staff Papers, International Monetary Fund, Vol. 9, pp. 369–79
[26]R. Garcia, P. Perron (1996), “An Analysis of the real interest rate under regime shifts,” Review of Economics and Statistics, Vol. 78, pp. 111-125.
[27]R.D. Henriksson, R.C. Merton (1981), “On Market Timing and Investment Performance 2: Statistical Procedures for Evaluating Forecasting.” Journal of Business, Vol. 54, pp. 513-533.
[28]M.T. Hagan, H.B. Demuth, M. Beale (1995), Neural Network Design. PWS publishing company, Boston, MA.
[29]G.P. Hopper (1997), “What Determines the Exchange Rate: Economic Factors or Market Sentiment?” Business Review-Federal Reserve Bank of Philadelphia, Sep/Oct., pp.17-29.
[30]K. Hornik, M. Stinchcombe, H. White (1989), “Multilayer Feedforward Networks are Universal Approximates,” Neural Network, Vol. 2, pp. 359-366.
[31]T. Jasic, D. Wood (2003), “Neural Network Protocols and Model Performance,” Neurocomputing, Vol. 55, pp. 747-753.
[32]F.L. Joutz, H.O. Stekler (1998), “Data Revisions and Forecasting,” Applied Economics, Vol. 30, pp.1011-1016.
[33]L. Kilian, M.P. Taylor (2003), “Why is it so Difficult to Beat Random Walk Forecast of Exchange Rates?” Journal of International Economics, Vol. 60, pp. 85-107.
[34]C.M. Kuan, T. Liu (1995), “Forecasting Exchange Rates Using Feedforward and Recurrent Neural Network,” Journal of applied econometrics, Vol. 10, pp. 347-364.
[35]K.S. Lai (1990), “An Evaluation of Survey Exchange Rate Forecasting.” Economics Letters, Vol. 32, pp. 62-65.
[36]G. Leitch, J.E. Tanner (1995), “Professional Economic Forecasts: Are They Worth Their Costs?” Journal of Forecasting, Vol. 14, pp. 143-157.
[37]M.T. Leung, A.S. Chen, H. Daouk (2000), “Forecasting Exchange Rates Using General Regression Neural Networks,” Computers & Operations Research, Vol. 27, pp. 1093-1110.
[38]N. Mark (1995), “Exchange Rate and Fundamentals: Evidence on Long-horizon Predictability.” American Economic Review, Vol. 85, pp. 201-218.
[39]N.C. Mark, D. Sul (2001), “Nominal exchange rates and monetary fundamentals: evidences from a small post-Bretton Woods panel,” Journal of International Economics, Vol. 53, pp. 29-52.
[40]R.A. Meese, K. Rogoff (1983), “Empirical Exchange Rate Models of the Seventies: do They fit out of Sample?” Journal of International Economics, Vol. 14, pp. 3-24.
[41]R.C. Merton (1981), “On Market Timing and Investment Performance 2: Statistical Procedures for Evaluating Forecasting.” Journal of Business, Vol. 54, pp. 363-402.
[42]L.A. Metzler (1960), “The Process of International Adjustment under Conditions of Full Employment: A Keynesian View,” published in R.E. Caves and H.G. Johnson, eds., Readings in International Economics (Irwin Homewood, 1968) pp.465-486.
[43]R.A. Mundell (1962), “The Appropriate Use of Monetary and Fiscal Policy under Fixed Exchange Rates,” Staff Papers, International Monetary Fund, Vol. 9, pp. 70–79.
[44]R.A. Mundell (1962), “Capital Mobility and Stabilization Policy under Fixed and Flexible Exchange Rates, ”Canadian Journal of Economics and Political Science, Vol. 29, pp. 475–85
[45]L.E. Öller, B. Barot (2000), “The Accuracy of European Growth and Inflation Forecasts,” International Journal of Forecasting, Vol. 14, pp. 293-315.
[46]R. Ostermark (2000), “A Hybrid Genetic Fuzzy Neural Network Algorithm Designed for Classification Problems Involving Several Groups,” Fuzzy Sets and Systems, Vol. 14, pp. 311-324.
[47]M. H. Pesaran, and A. Timmermann (1992), “A Simple Nonparametric Test of Predictive Performance,” Journal of Business & Economic Statistics, Vol. 10, No. 4, pp. 461-465.
[48]M.H. Pesaran, A. Timmermann (2004), “How Costly is it to Ignore Breaks when Forecasting the Direction of a Time Series?” International Journal of Forecasting, Vol. 20, pp. 411-425.
[49]J. Pons (2001), “The Rationality of Price Forecasts: a Directional Analysis,” Applied Financial Economics, Vol. 11, pp. 287-290.
[50]M. Qi (2001), “Predicting US Regressions with Leading Indicators via Neural Network Models,” International Journal of Forecasting, Vol. 17, pp. 383-401.
[51]M. Qi, Y. Wu (2003), “Nonlinear Prediction of Exchange Rates with Monetary Fundamentals,” Journal of Empirical Finance Vol. 10, pp. 623-640.
[52]M.H. Schnader, H.O. Stekler (1990), “Evaluating predictions of change.” Journal of Business, Vol. 63, pp. 99-107.
[53]H.O. Stekler (1994), “Are Economic Forecasts Valuable?” Journal of Forecasting, Vol. 13, pp. 495-505.
[54]J.H. Stock, M.W. Watson (1996), “Evidence on Structural Instability in Macroeconomic Time Series Relations,” Journal of Business and Economic Statistics, Vol. 14, pp. 11-30.
[55]M.E. Thomson, D.O. Atay, A.C. Pollock, A. Macaulay(2003), “The Influence of Trend Strength on Directional Probabilistic Currency Predictions,” International Journal of Forecasting, Vol. 19, pp. 241-256.
[56]A. Timmermann (2001), “Structural Breaks, Incomplete information, and Stock Prices,” Journal of Business and Economic Statistics, Vol. 19, pp. 299-314.
[57]A. Trapletti, A.Geyer, F. Leisch(2002), “Forecasting Exchange Rate Using Cointegration Models and Intra-day Data,” Journal of Forecasting, Vol. 21, pp. 151-166.
[58]H. Tsukilmoto, H. Hatano (2003), “The functional Localization of Neural Networks Using Genetic Algorithms,” Neural Networks, Vol. 16, pp. 55-67.
[59]T.S. Wirjanto (1999), “Empirical Indicators of Currency Crises in East Asia,” Pacific Econ., Rev. 4, pp. 165-183.
[60]B. Wu (1995), “Model-free Forecasting for Nonlinear Time Series (with Application to Exchange rates),” Computational Statistics & Data Analysis, Vol. 19, pp. 433-459.
[61]Y. Wu, H. Zhang (1997), “Forward Premiums as Unbiased Predictors of Future Currency Depreciation: a non-parametric analysis,” Journal of Money and Finance, Vol. 16, pp. 609-623.
[62]G.P. Zhang, V.L. Berardi (2001), “Time Series Forecasting with Neural Network Ensembles: an Application for Exchange Rate Prediction,”Journal of Operational Research Society, Vol. 52, pp. 652-664.
[63]G. Zhang, M.Y. Hu (1998), “Neural Network Forecasting of the British Pound/US Dollar Exchange Rate,” Omega, Vol. 26, No. 4, pp. 495-506.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top