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。

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.

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