我們把經濟體視為一個複雜適應系統(complex adaptive system), 強調系統中異質性(heterogeneous)agent的學習適應行為與agent之間的互動性交互作用, 此時主流經濟學裡的分析架構, 如:代表性個人模型(represesentive agent model)、理性預期(rational expectation)、固定點均衡分析(fixed-point equilibrium analysis)等將不再適用, 取而代之的是演化經濟學(evolutionary economics)的研究典範, 這樣的研究架構下, 並沒有適當的數學分析工具可資運用, 因此我們改以agent-based建模(agent-based modelng)的社會模擬(social simulation)來建構一個人工的經濟體(artificial economy), 以此為主要研究方法, 這就是agent-based計算經濟學(agent-based computational economics)或稱人工經濟生命(artificial economic life)。 本文中以股票市場為主要的研究課題, 我們以遺傳規劃(genetic programming)的人工智慧(artificial intelligence)方法來模擬股市中有限理性(bounded rational)異質交易者的交易策略學習行為, 建構出一個人工股票市場(artificial stock market), 在這樣的架構下, 我們成功地產生出類似真實股票市場的股價時間序列特性, 我們同時也檢定了人工股票市場中價量的因果關係, 說明了在沒有外生因素之下, 人工股票市場的複雜系統可自發地產生出雙向的價量因果關係, 進一步地, 我們研究下層agent(交易者)行為與上層股價時間序列行為的關聯性, 我們也發現個體的行為並不能直接加總或推論出複雜適應系統的總體行為, 這就是突現性質(emergent property)的發生, 最後, 本文描述了agent-based計算經濟學研究架構的優勢與缺點, 再附帶介紹一個用以進行agent-based建模相關研究的軟體程式庫-SWARM。

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.

知訊者與非知訊者資訊不對稱之議題在學術殿堂一直廣為學者所研究討論，且各國證管機關為了維持證券市場公平性、保護非知訊者權益並且維持股票市場的穩定運作，適度的資訊揭露以維持證券市場的公平性一直都是各國證券交易所重視的政策目標。 是故，本研究利用代理人基人工股票市場來探討資訊揭露對於金融市場之影響。在此架構下之交易者皆已有限理性方式來呈現。他們是以遺傳規劃(genetic programming)之方式來學習並修正他們對於未來之金融市場之預期。在透過即時的模擬價格之資訊揭露，我們嘗試探討此資訊揭露之金融政策措施對於市場之波動性、市場之流動性之影響。 / The topic of asymmetric information between the informed traders and uninformed traders has been widely discussed by researchers in academics. To maintain the fairness of securities market, an appropriate information disclosure is quite important for authorities of securities regulation to protect the rights and interests of uninformed traders, and to maintain the operations of securities market stable. Based on these reasons, we construct an agent-based artificial stock market to investigate how information disclosure affects a financial market. In this framework of artificial stock market, all traders are characterized by bounded rationality. The traders are able to learn and adjust their predictions of financial market by means of a genetic programming algorithm. We try to understand how market transparency affects the volatility and the liquidity of a securities market.