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標題: 探討技術分析於GARCH模型預測外匯波動度之成效-以日幣、墨西哥比索為例
Examining the Performance of FX Volatility Forecasting in Using Technical Analysis-Evidence from Japanese Yen and Mexican Peso
作者: 陳怡安
Yi-An Chen
關鍵字: 外匯;技術分析指標;GARCH 模型;波動率預測;逐步預測力優劣檢定法;Foreign Exchange;GARCH Model;SSPA Test;Technical Analysis;Volatility Forecasting
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本研究之主要目的為探討使用技術分析於 GARCH 模型,是否改善對於
外匯波動率之預測能力。首先,是以實現波動率為數據資料,採用四大類技術分析指標,如為移動平均線(Moving Average,以下簡稱 MA)、濾嘴法則(Filter Rules,以下簡稱 FR)、支撐與壓力(Support and Resistance,以下簡稱 SR)以及通道突破(Channel Breakout,以下簡稱 CB),使用技術交易規則來產生交易訊號,將其應用於兩種外匯匯率,包括成熟市場的日幣與新興市場的墨西哥比索,再將交易訊號加入於以日報酬率為數據資料的 GARCH 模型之條件變異數內進行參數估計,估計方法採用最大概似估計法(Maximum likelihood),並選擇逐日滾動之方法進行預測。最後,為了避免資料探勘偏誤之問題,選擇逐步預測力優劣檢定挑出可擊敗 GARCH 基準模型之本研究模型,其中將 MAE 和 MSE 作為評價基準建立損失函數。實證結果發現本研究使用技術分析調整後,在成熟市場與新興市場皆有少部分競爭模型對波動度之預測可顯著擊敗GARCH 基準模型。

The main purpose of this paper is to explore technical analysis to improve GARCH model of predictive ability in the foreign exchange volatility. First, we use realized volatility as data and apply four technical indicators to produce technical trading signals. For example, Moving Average, Filter Rules, Support and Resistance and Channel Breakout. And we can apply to two different foreign exchange rates, including the mature markets of JPY and emerging markets of MXN. Then we add these trading signals to the conditional variance of GARCH model with daily return data and the parameter estimation is performed. For estimating the parameters of GARCH model, estimation method uses the Maximum likelihood estimation and rolling-window forecast method. Finally, in order to avoid the data snooping problem, we choose the SSPA test to evaluate the effectiveness of forecasting, by using the measurements of MAE and MSE as two evaluation criteria to build up the
loss functions. After adjusting GARCH model, our results show adjusted GARCH model in mature market and emerging markets can outperform the benchmark model.
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