本文研究主旨在討論總體經濟變數是否可以作為台灣股價指數波動度的解釋因子，本文使用線性預測模式，在樣本內與樣本外分析架構下，討論台灣加權股價指數的報酬率波動度是否在加入總體經濟變數後，可以提升模型解釋能力，並且考慮國內外文獻中有討論的財務變數及外匯變數進入模型，觀察台灣實證經驗是否與成熟市場的美國經驗一致。
在實證結果上，與Paye(2012)使用美國S&P500 指數資料所得出的結果相似，無顯著證據顯示將總體經濟變數納入模型可以改善預測能力，單一變數中，僅有週轉率變數與利率變數的預測能力較佳。此外，個別模型預估值的線性組合方法較能改善模型預測能力，顯示多方訊息的彙整下，含有豐富的市場資訊，可以有效預測股價市場的波動。

Aggregate stock volatility is changing anytime. The main topic of this thesis tests whether it is possible to improve the stock market volatility forecasts by adding the macroeconomics variables into the linear model. The paper uses the linear predictable model to construct the in-sample and out-of-sample analysis forecast model to test whether the model considering macroeconomics variables outperforms the benchmark model. It is difficult to find the empirical evidence that forecasts on conditioning an macroeconomic variable outperform than the volatility lagged information. Only the financial variables which are Taiwan stock monthly turnover rate and credit variables have improved the model forecast. The most successful approaches involve simple combination of individual forecasts.

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