本研究之主要目的是在探討技術分析能否改進一般化自我迴歸條件異質變異數(GARCH(1,1))模型對股價指數波動度的預測能力。研究採用四大技術指標：濾嘴法則、支撐與壓力、通道突破、移動平均共1,107條技術規則，並分別應用於股價指數的實現波動率產生訊號，探討以技術分析訊號擴充後的GARCH(1,1)模型能否提升基準模型(GARCH(1,1))的波動度預測力；並以平均絕對誤差（MAE）與均方誤差（MSE）為評比基準，利用Hsu, Kuan, and Yen (2014)的Step-SPA(k)檢定法來降低資料窺探偏誤。本研究以2008年至2012年與1993年至2012年的道瓊工業平均指數資料為樣本，結果發現以技術規則分析實現波動率訊號對GARCH(1,1)模型預測股價指數波動率能力並無顯著提升之效果。

The main purpose of this study is to explore whether technical analysis can improve the predictability of Generalized Autoregressive Conditional Heteroscedasticity (GARCH(1,1)) for the volatility of stock index. Four technical analysis categories, filter rule, support and resistance, channel breakout, and moving average, were applied, creating 1,107 rules in total, and the rules were used individually on the realized volatility of stock index to obtain signals. We discuss if the GARCH(1,1)-augmented model with technical analysis signals can provide better predictability for stock index volatility than the benchmark model(GARCH(1,1)). Employing the mean absolute error (MAE) and mean squared error (MSE) as performance measure, we conduct Hsu, Hsu and Yen’s (2014) Step-SPA(k) test to control for the data snooping bias. Dow Jones Industrial Average data from 2008 to 2012 and 1993 to 1992 are used as the sample. Our results showed that analyzing realized volatility signals with technical rules does not lead to a significant improvement in the predictability of the GARCH(1,1) model for volatility of stock indices.

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