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系統識別號 U0026-1207201316415200
論文名稱(中文) 探討技術分析是否能提升GARCH模型對外匯波動度的預測能力
論文名稱(英文) Can Technical Analysis Improve the Ability of the GARCH Model to Predict Foreign Exchange Volatility?
校院名稱 成功大學
系所名稱(中) 財務金融研究所
系所名稱(英) Graduate Institute of Finance
學年度 101
學期 2
出版年 102
研究生(中文) 賴昱廷
研究生(英文) Yu-Ting Lai
學號 r86001102
學位類別 碩士
語文別 英文
論文頁數 26頁
口試委員 指導教授-顏盟峯
共同指導教授-劉裕宏
口試委員-王凱立
口試委員-曾翊恆
中文關鍵字 技術分析  ㄧ般化自我迴歸異質變異數模型  外匯波動度  廣義逐步預測力優劣檢定 
英文關鍵字 Technical analysis  GARCH  Foreign exchange volatility  Generalized Stepwise Superior Predictive Ability test 
學科別分類
中文摘要 本研究的主要目的是在檢驗技術分析在外匯波動率的預測能力與獲利能力。本篇研究採用五大類技術指標(FR、SR、CB、MA、MOM),將共計1,085條的技術交易規則應用到4種外匯匯率(澳幣、歐元、英鎊、日幣)。我們採用以1,085條的技術交易規則擴充後的一般化自我相關條件異質變異模型 (GARCH(1,1) model)為比較模型,使用廣義逐步預測力優劣檢定(Generalized Stepwise Superior Predictive Ability test, SSPA(k) test)於樣本期間內探討是否比較模型對外匯波動度的預測力表現優於基準模型。本文想探討是否加入技術分析指標能進一步提升GARCH(1,1)對外匯波動度的預測力,並放在Hsu, Kuan and Yen(2013)的廣義逐步預測力優劣檢定方法來進行檢定,以避免資料探勘偏誤(data snooping bias)問題。因此,我們能夠在當資料探勘偏誤(data snooping bias)不存在下,討論在不同的幣別和不同的樣本期間中加入技術分析是否能有效地提升波動度模型對外匯波動度的預測能力。
英文摘要 This paper examines the predictive and profitable ability of technical analysis in the foreign exchange volatility. We apply five technical indicators (FR, MA, CB, SR and MOM) which total 1,085 technical trading rules to four different foreign exchange rates (AUD, EU, GBP and JPY). We use the augmented GARCH(1,1) model with 1,085 technical trading rules as competing model and then exam whether the foreign exchange volatility predictability of competing model outperforms the benchmark model in the whole sample period by Generalized Stepwise Superior Predictive Ability test (SSPA(k) test). This paper would like to discuss whether technical analysis indicators can improve the ability of the GACH model to predict the foreign exchange volatility, also we have to test it by using SSPA(k) test according to Hsu, Kuan and Yen(2013) in order to avoid the data snooping bias. As a result, we are able to provide a big picture of the effectiveness of technical analysis in forecasting foreign exchange volatility across different currencies and periods without data snooping bias.
論文目次 摘要 I
Abstract II
I. Introduction 1
II. Literature Review 2
III. Methodology 6
3.1. Return and Realized Volatility 6
3.2. Technical indicators 7
3.3. In-sample Estimation 10
3.4. Out-of-sample Forecasting Performance 11
3.5. Generalized Stepwise SPA test 13
IV. Empirical Results 16
4.1. Data 16
4.2. Out-of-sample Performance by monthly data 17
4.3. Out-of-sample Performance by daily data 18
V. Conclusions 19
Appendix 21
References 24
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