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研究生:趙威豪
研究生(外文):Wei-HaoTsao
論文名稱:探討技術交易規則對外匯波動率的增額預測資訊內涵
論文名稱(外文):Can technical rules provide incremental information content for predicting FX volatility?
指導教授:顏盟峯顏盟峯引用關係
指導教授(外文):Meng-Feng Yen
學位類別:碩士
校院名稱:國立成功大學
系所名稱:財務金融研究所
學門:商業及管理學門
學類:財務金融學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:23
中文關鍵詞:技術分析交易規則資料探勘偏誤逐步真實性檢定定態拔靴
外文關鍵詞:technical analysistrading ruledata snooping biasSRC teststationary bootstrap
相關次數:
  • 被引用被引用:1
  • 點閱點閱:187
  • 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
為檢驗技術分析其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交易規則(FR、SR、CB、MA、MOM)共計(1,085條)應用到3種外匯匯率(日幣、英鎊、澳幣),使用逐步真實性檢定(Stepwise Reality Check, SRC test)於樣本期間內作探討。過去文獻從未有研究結合技術交易指標和波動度GARCH模型,本文想探討是否加入技術指標能進一步提升GARCH(1,1)的預測力,並放在Romano, Shaikh and Wolf的Generalized-k Step RC中來做檢定,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,討論技術交易規則在不同的幣別和不同的樣本期間中是否能有效地預測外匯波動率。
To examine the predictive and profitable ability of technical analysis in the foreign exchange volatility, we apply 1,085 technical trading rules of five rules families(FR、SR、CB、MA、MOM)to three different foreign exchange rates, then we exam the whole sample period by Stepwise Reality Check (SRC)test. Literature has never been studies to combine the technical trading indicators and GARCH model before. This paper would like to discuss whether joining the technical indicators can further enhance the predictive power of GARCH(1,1)model or not, also we have to test it by using Generalized-k Step RC(Romano, Shaikh and Wolf)in order to avoid the data snooping bias. As a result, we are able to provide a big picture of the effectiveness of technical trading rules in forecasting foreign exchange volatility across different currencies and periods without data snooping bias.
第一章 緒論 .............................................................................................1
第一節 研究背景與動機.......................................................................1
第二節 研究目的...................................................................................1
第三節 研究架構...................................................................................2
第二章 文獻探討 .....................................................................................2
第三章 研究方法 .....................................................................................7
第一節 報酬率與已實現波動率...........................................................7
第二節 交易規則...................................................................................8
第三節 樣本內估計..............................................................................12
第四節 樣本外預測績效......................................................................13
第五節 逐步真實性檢定法..................................................................14
第四章 實證結果 ....................................................................................17
第一節 外匯匯率的波動......................................................................17
第二節 波動性指標和相關產品..........................................................17
第三節 廣義逐步真實性檢定之結果..................................................17
第五章 結論與未來研究建議..................................................................19
第一節 結論..........................................................................................19
第二節 未來研究建議.........................................................................20
參考文獻 ..................................................................................................22
1.Andersen, T.G. and T. Bollerslev (1998) “Deutsche Mark-Dollar Volatility:Intraday Activity Patterns, Macroeconomic Announcements, and Longer Run Dependencies, Journal of Finance, 53, 219-265.
2.Andersen, T.G. and T. Bollerslev and S. Lange (1999) “Forecasting Financial Market Volatility:Sampling Frequency vis-à-vis Forecast Horizon, Journal of Empirical Finance, 6, 457-477.
3.Baillie, R.T. and T. Bollerslev (1991) “Intra Day and Inter Day Volatility in Foreign Exchange Rates, Review of Economic Studies, 58, 565-585.
4.Constructing and testing a commodity portfolio. Journal of Futures Markets 25: 643-660 Marshall, B.R., Cahan, R.H., and Cahan, J.M., 2008a. Can commodity futures be profitably traded with quantitative market timing strategies? Journal of Banking and Finance 32: 1810-1819.
5.Leuthold, R.M., 1972. Random walk and price trends: the live cattle futures market. Journal of Finance 27: 879-889 Lukac, L.P., and Brorsen, B.W., 1990. A comprehensive test of futures market disequilibrium. Financial Review 25: 593-622.
6.Park, C.-H., and Irwin, S.H., 2010. A reality check on technical trading rule profits in the U.S. futures markets. Journal of Futures Markets 30: 633-659 Szakmary, A.C., Shen, Q., and Sharma S.C., 2010. Trend-following trading strategies in commodity futures: A re-examination. Journal of Banking and Finance 34: 409-426.
7.Roberts, M.C., 2005. Technical analysis and genetic programming:
8.Romano, J.P., Shaikh, A. M., Wolf, M. (2008) “Formalized Data Snooping Based on Generalized Error Rates, Econometric Theory, 24, 404-447.
9.Stevenson, R.A., and Bear, R.M., 1970. Commodity futures: trends or random walks? Journal of Finance 25: 65-81.
10.Silber, W.L., 1994. Technical trading: when it works and when it doesnt. Journal of Derivatives 1: 39-44.


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