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題名：台灣50指數股票型基金(ETF)對標的指數成分股之影響
作者：林靖中 引用關係
作者(外文)：Ching-Chung Lin
校院名稱：國立成功大學
系所名稱：企業管理學系碩博士班
指導教授：江明憲
學位類別：博士
出版日期：2005
主題關鍵詞：波動性；流動性；指數股票型基金；台灣50指數；定價效率；Taiwan 50 Index；volatility；pricing efficiency；liquidity；ETF
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　　台灣50 ETF為台灣的第一支ETF商品，於93/6/30開始上市買賣，其標的資產是以台灣股票市場中市值前五十大公司為取樣對象的台灣50指數。為了解台灣50 ETF上市對金融市場的影響，本文主要的研究目的為：(1)說明指數股票型基金的市場運作與商品特性以及分析台灣50 ETF的定價效率、(2)從各種不同的流動性定義的角度，探討台灣50指數成分股的流動性在台灣50 ETF上市之後是否有所改變；(3)利用不同的波動性衡量指標與模型，研究台灣50 ETF上市是否會對台灣50指數成分股的波動性造成影響、以及(4)利用橫斷面分析，分析不同市值規模與產業類別的公司是否會受到不同程度的影響。
　　爲探討台灣50 ETF的定價效率，本文利用92/6/30到93/6/30的台灣50 ETF每日淨資產價值與市價資料，分別以迴歸、溢折價百分比、以及絕對錯誤定價百分比來衡量其淨資產價值與市價間的關係。92/4/1到92/9/30的日內五分鐘資料用以探討台灣50指數成分股的流動性與波動性在台灣50 ETF上市後的改變情形，指數成分股的流動性指標為買賣價差檔數、成交量(值)、Amivest流動性比率、以及Martin流動性比率，指數成分股波動性的衡量則是利用GK (1980)波動性、ABDL (2001)波動性以及GARCH模型的非條件變異數。流動性與波動性指標的計算中需要使用價格數列的部分，均分別利用日內五分鐘成交價格以及買賣價中點價格計算相關指標。本研究也根據市值規模與產業類別將公司分別區分為三個類別，以探討不同特性公司的流動性與波動性變化是否有所差異。
　　本研究重要的實證結果與意涵如下：
(1)台灣50 ETF的平均溢價百分比為0.041%，但是此溢價幅度並不顯著。台灣50 ETF的平均絕對錯誤定價百分比為顯著的0.383%，但在考慮套利成本之後，這樣的幅度並沒有經濟上的意義。因為台灣50 ETF的定價效率相當高，並未出現如同封閉型基金一樣的高折價現象。
(2)台灣50指數成分股的流動性在台灣50 ETF上市之後有增加的傾向，這和套利假說學派的預期相符。該學派認為因為籃子證券可以提供指數套利的機會，因此會導致標的資產的流動性增加。不同的市值規模公司的流動性變動情形並沒有差異，但是存在證據支持電子類股與金融類股的流動性增加的現象比其他類股強烈。
(3)台灣50指數成分股的波動性在台灣50 ETF上市之後有顯著增加的現象，這可能是因為台灣50 ETF的交易增加了資訊流動而使得指數成分股的波動性增加。不同規模公司間的波動性增減情形是沒有差異的，不過本研究發現強烈的證據支持不同產業間的波動性變動情形存在顯著的差異：電子類股與金融類股的波動性是增加的，其他類股則呈現下降的現象。
　　The Taiwan Stock Exchange (TSE) introduced the first Taiwanese ETF --- “Taiwan Top 50 Tracker Fund” (TTT) on June 30, 2003. The underlying index of TTT is the Taiwan 50 Index, which is calculated using the trade prices of the largest 50 listed companies by total market value. To understand the impact of TTT on the financial market, the purpose of this dissertation is: (1) to study the pricing efficiency of TTT, which is defined as the difference between the net present value and the market value of TTT; (2) to investigate the liquidity and the volatility changes of the component stocks of the Taiwan 50 Index by using different measurements of liquidity and volatility; and (3) to explore whether the component companies with different characteristics have different changing patterns.
　　To investigate the pricing efficiency of TTT, the daily data of net present values and market prices of TTT from 2003/6/30 to 2004/6/30 are used. The five-minute intraday data from 2003/4/1 to 2003/9/30 is employed to explore the liquidity and the volatility change of the component stocks after the introduction of TTT. This study uses the methodology of regression, the premium percentage, and the absolute mispricing percentage to explore the pricing efficiency of TTT. The bid-ask tick, trading volume and value, Amivest liquidity ratio, and Martin liquidity ratio are employed to measure the liquidity of component stocks of TTT. To evaluate the volatility, the GK (1980) volatility, ABDL (2001) volatility, and unconditional variance of the GARCH model have been computed. The component stocks have been categorized into three groups based on market value or industry to explore whether different categories have different changing levels.
　　The findings and implications of these empirical results are as follows：
(1)The empirical result shows that TTT is pricing efficient. The average premium percentage is an insignificant figure of 0.041%. Although the average absolute mispricing percentage of 0.383% is statistically significant, it is not economically significant after considering the arbitrage costs.
(2)The liquidity of constituents tends to increase after the introduction of TTT. This is coincidental with the expectation of the index arbitrage hypothesis, which believes that basket security will increase the liquidity of underlying assets because it provides the opportunity of arbitrage. The liquidity changes of different market-value categories are the same. However, evidence supports that the liquidity increases of the electronic and the financial sectors tend to be more.
(3)The volatility of component stocks significantly increases after the introduction of TTT. The explanation is that the transaction of TTT increases the information flow and thus the volatility. Still, the changing-patterns of different market-value categories are similar. In addition, this study finds strong evidence showing that the volatilities of the electronic and the financial sectors increase, while that of other industry decreases.
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