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研究生:陳玉鎔
研究生(外文):Yu-Rong Chen
論文名稱:以植基於GARCH、EGARCH及GJR-GARCH之PSO方法建構匯率預測模型
論文名稱(外文):The Construction of PSO based GARCH, EGARCH and GJR-GARCH Model for Forecasting Exchange Rate
指導教授:張瑞芳張瑞芳引用關係
指導教授(外文):Jui-Fang Chang
學位類別:碩士
校院名稱:國立高雄應用科技大學
系所名稱:國際企業系
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:101
畢業學年度:100
語文別:中文
論文頁數:109
中文關鍵詞:GARCHEGARCHGJR-GARCH粒子群演算法匯率
外文關鍵詞:GARCHEGARCHGJR-GARCHPSOExchange rate
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在研究上,時間序列之匯率預測一直是當今一項重要的研究議題,但模型選擇存在著精確性與處理時效是項艱難的問題。本研究利用傳統時間序列模型GARCH、EGARCH及GJR-GARCH,與新建構模型PSOGARCH、PSOEGARCH及PSOGJR-GARCH,透過追蹤誤差對匯率預測進行比較,期望建構準確的匯率預測模型。
本文主要目的為建構預測能力較高的計量模型,實驗一引用Chang and Tzeng(2009)挑選對匯率影響的27個變數,將測試時間分為二年、四年及六年,並以傳統時間序列模型GARCH、EGARCH及GJR-GARCH進行估計與預測,實驗二採用最佳化技術PSO模型所選取之十項變數,進而將變數投入傳統時間序列模型,即建構三個模型PSOGARCH、PSOEGARCH及PSOGJR-GARCH進行估計與預測,實驗三將上述六者模型進行追蹤誤差比較。
最後結果顯示經由PSO模型篩選10個變數進行估計與預測,即有良好的預測績效,且以本文建構之 PSOGJR-GARCH模型具有最小的預測誤差,亦即顯示預測能力最佳,其次為PSOEGARCH模型,傳統模型GJR-GARCH略優於EGARCH,GARCH模型之預測能力則為最差。
Forecasting exchange rates by using time series has been an important research topic recently. The accuracy and processing time of models have always been of great concern during model selection. This study uses traditional time series models GARCH, EGARCH and GJR-GARCH, and newly constructed models PSOGARCH, PSOEGARCH and PSOGJR-GARCH, to compare exchange rate forecasts through tracking errors in the hopes of constructing an accurate exchange rate forecasting model.
The main purpose of this study is to construct a measurement model with high forecasting abilities. Experiment one uses the method introduced by Chang and Tzeng (2009) in the selection of 27 variables that affect exchange rates. The test time is separated into two years, four years and six years, and the traditional time series models GARCH, EGARCH and GJR-GARCH are used for forecasting. Experiment 2 uses the 10 variables selected by the optimization PSO model and inputs them into the traditional time series models to construct new models PSOGARCH, PSOEGARCH and PSOGJR-GARCH for forecasting. Experiment 3 compares the tracking error of the above six models.
The results show that exchange rate forecasts from the PSO model have great predictability. The PSOGJR-GARCH model built in this study has minimum forecasting errors and displays the best forecasting ability, followed by the PSOEGARCH model. The traditional model, GJR-GARCH, is slightly better than EGARCH; GARCH is the worst model for forecasting.
中文摘要i
Abstractii
誌 謝iv
目 錄v
表目錄vii
圖目錄ix
第一章 緒論1
第一節 研究動機與背景1
第二節 研究目的2
第三節 研究架構及流程3
第二章 文獻探討5
第一節 匯率決定理論5
第二節 粒子群演算法文獻回顧 10
第三節 時間序列預測文獻回顧 12
第四節 結合 PSO 與時間序列模式文獻回顧16
第三章 研究方法19
第一節 常態性與序列相關檢定 19
第二節 單根檢定20
第三節 粒子群演算法22
第四節 時間序列預測模型27
第五節 移動視窗法30
第六節 評估預測誤差績效32
第七節 實驗設計32
第四章 實證結果與分析42
第一節 實證資料42
第二節 實驗一45
第三節 實驗二66
第四節 實驗三85
第五章 結論與建議88
第一節 研究結論88
第二節 研究貢獻89
第三節 研究限制與未來建議89
參考文獻91
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