本文主要在探討影響台灣股票市場波動的因素，除了考慮以之前學者設定的 VAR(12)模型研究，另外以 SUR(5)模型來討論股市波動與基本面、交易面間的關係；最後，再以自我迴歸異質條件變異數模型來分析股市波動的特性。最重要的是，我們會根據誤差項的各類檢定結果來判定研究股市波動性質的最佳模型。

在聯立方程式的估計中，我們發現代表資訊到達指標的兩變數--週轉率與成交量成長率--會影響股票市場的波動。另外，我們找出交易面（成交量成長率）可能會影響基本面（匯率），這也就是說，在研究股市波動時，我們不需要特別區分變數的屬性。

在 GARCH 模型及 TGARCH 模型中，我們仍然可發現週轉率與成交量成長率會影響股市條件平均數或條件變異數；除此之外，好壞消息對股市日報酬率條件變異數（條件波動）應有不同的影響效果（壞消息的影響力較快反應）。而股市自身風險係數雖然統計檢定上不顯著異於零，但若未加入條件平均數的估計式，則可能會使模型得到較差的誤差項檢定結果，顯見股市自身風險應為影響投資人設定期望報酬率水準的重要因素之一。

從上述估計結果，我們可以知道，若散戶投資人能正確解讀市場上出現的各種新資訊之背後意義，將可使成交量成長率或週轉率（大部份可能代表無意義或不正確的交易行為）的變動幅度降低，進而有效地減少股票市場中股價異常波動的現象。

My essay's topic focuses on discussing the factors that influence stock market volatility in Taiwan's stock market. Besides VAR(12) model as previous researchers have studied, I tries to set up SUR(5) models analyzing the relationship among the stock market volatility、the foundamental variables'volatilities and trading activities; Then I cited ARCH models ( autoregressive conditional heteroskedisticity models ) to find out the characteristics of stock market volatility. Most important of all, according to each misspecification test ( residual test ), I would specify the better models to describe the stock market volatility.

In the estimations of system equations ( VAR(12)and SUR(5)models ), first I found that turnover rate and the growth rate of trading volume, which represent the information arrival indexes, could effect stock return's monthly conditional variance. Second, I especially found out the evidence that trading activities (trading volume growth) would probably have an impact on the macroeconomic variable ( exchange rate volatility ). It shows that we don't need to distinguish the attributes of those factors which could influence stock market volatility.

In GARCH and TGARCH model, the positive influences of turnover and trading volume growth on daily stock return's conditional mean and conditional variance ( conditional volatility ) are still obvious, Within these TGARCH model, I discovered that bad news and good news could have different influences on stock market volatility ( the impact of bad news which resulted in downward movements of stock market volatility appeared faster that the good news'which caused upward movements). Stock market's self-risk(σ_t-1^{^2}) is statistically insignificant different from zero in GARCH models, but when I omitted this variable in daily stock return's conditional mean estimation equation, standardized residual might not obey the assumption of normal distribution. It apparently told us that the stock market's self-risk term ( σ_t-1^{^2} ) is one of the critical factors which influences investors to estimate expected return level.

From those results above, we realized that if investors could precisely understand the real meanings of new information conveying in the stock market, it might decrease the levels of turnover and trading volume growth ( which could sometimes represent meaningless or inexact trading activities ), then effectively reduce the abnormal volatility phenomenon in stock market.