政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/140606

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/140606

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政大典藏 > College of Commerce > Department of Money and Banking > Theses > Item 140.119/140606

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/140606

Title:	中國大陸ETF追蹤誤差之研究——以金融行業為例 Research on the Tracking Errors of ETF in China – Empirical Analysis of Financial Industry
Authors:	李文婕 Li, Wen-Jie
Contributors:	陳威光李文婕 Li, Wen-Jie
Keywords:	股票指數型基金 ETF 追蹤誤差 Exchange traded fund ETFs Tracking errors
Date:	2022
Issue Date:	2022-07-01 16:11:06 (UTC+8)
Abstract:	隨著近幾年ETF在中國大陸金融市場的飛速發展，越來越多的資金流入市場，行業ETF越來越受到投資者的歡迎。截至2021年底，中國A股市場上市的ETF數量達到了641檔，規模超過了1.4萬億人民幣。本文以中國大陸金融行業ETF為研究對象對其的追蹤誤差進行研究，採用多種方法度量各檔ETF的追蹤誤差，並從不同的角度對各檔ETF進行橫向對比，最後通過實證分析探究哪些因素會對追蹤誤差產生影響。得到了以下主要結論：當通過平均值追蹤誤差法衡量ETF追蹤誤差時，首先發現14檔ETF中有12檔的平均追蹤誤差值為正，僅2檔ETF追蹤誤差為負。進一步觀察2020至2021年間的正/負追蹤誤差頻率時，有7檔有100%正追蹤誤差頻率，最低的正追蹤誤差頻率也有82.80%。平均14檔樣本資料中正追蹤誤差百分比高達97.52%。以業界的規定的風險控管臨界值不超過0.2%作為日追蹤偏離度絕對值的衡量依據，發現14檔中有其中有5檔偏離的頻率在1%之下，11檔偏離臨界值的機率低於5%。另外用業界的年標準差追蹤誤差不高於2%為標準來判斷時，在2020-2021年間有11檔沒有超出臨界值，只有4檔超出範圍。對追蹤誤差影響因素做實證分析，發訊成交量、複製策略、資產規模、指數波動率變數顯著影響ETF的追蹤誤差，而指數成分股變動率折溢價率的結果並不顯著。 With the rapid development of ETF in the Chinese mainland financial market in recent years, a large amount of funds into the market, ETF is more and more popular with investors. By the end of 2021, the number of ETFs listed on China`s A-share market reached 641, with A total value of more than 1.4 trillion yuan. This paper takes ETFs in the financial industry in mainland China as the research object to study the tracking error of ETFs, adopts a variety of methods to measure the tracking error of ETFs, and makes a horizontal comparison of ETFs from different perspectives. Finally, empirical analysis is conducted to explore which factors will have an impact on the tracking error. The main conclusions are as follows: When the average tracking error method is used to measure the tracking error of ETFs, it is first found that the average tracking error value of 12 out of 14 ETFs is positive, and only 2 ETFs are negative. When looking further at the positive/negative tracking error frequency from 2020 to 2021, seven tracks had 100% positive tracking error frequency, and the lowest positive tracking error frequency was 82.80%. The average positive tracking error percentage of 14 samples is 97.52%. The absolute value of daily tracking deviation was measured by the industry risk control threshold not exceeding 0.2%. It was found that 5 of the 14 ETFs deviated less than 1% of the time, and 11 ETFs deviated less than 5% of the time. In addition, when the industry`s annual standard deviation tracking error is no more than 2%, there are 11 ETFs that do not exceed the critical value in 2020-2021, and only 4 ETFs that exceed the range. Empirical analysis of the factors influencing the tracking error shows that the transaction volume, replication strategy, asset size and index volatility variables significantly affect the tracking error of ETF, while the change rate and discount rate of index components are not significant.
Reference:	[1] Beasley, J. E., Meade, N., & Chang, T. J. (2003). An evolutionary heuristic for the index tracking problem. European Journal of Operational Research, 148(3), 621-643. [2] Buetow, G. W., & Henderson, B. J. (2012). An empirical analysis of exchange- traded funds. The Journal of Portfolio Management, 38(4), 112–127. [3] Chu, P. K. K. (2011). Study on the tracking errors and their determinants: Evidence from Hong Kong exchange traded funds. Applied Financial Economics, 21(5), 309–315. [4] Chu, P. K. K. (2016). Analysis and forecast of tracking performance of Hong Kong exchange-traded funds: Evidence from tracker fund and X iShares A50. Review of Pacific Basin Financial Markets and Policies, 19(04), 1650022. [5] Dorocáková, M. (2017). Comparison of ETF s performance related to the tracking error. Journal of International Studies, 10(4), 154-165. [6] Engle, R. F., & Sarkar, D. (2006). Premiums-discounts and exchange-traded funds. ETFs and Indexing, 2006(1), 35–53. [7] Frino, A., & Gallagher, D. R. (2001). Tracking S&P 500 index funds. The Journal of Portfolio Management, 28(1), 44–55. [8] Frino, A., & Gallagher, D. R. (2002). Is index performance achievable? An analysis of Australian equity index funds. Abacus, 38(2), 200–214. [9] Frino, A., Gallagher, D. R., Neubert, A. S., & Oetomo, T. N. (2004). Index design and implications for index tracking. The Journal of Portfolio Management, 30(2), 89-95. [10] Poterba, J. M., & Shoven, J. B. (2002). Exchange-traded funds: A new investment option for taxable investors. American Economic Review, 92(2), 422-427. [11] Rompotis, G. G. (2009). Performance and trading characteristics of iShares: An evaluation. IUP Journal of Applied Finance, 15(7), 24.13. Roll, R. (1992). A mean/variance analysis of tracking error. Journal of Portfolio Management, 18(4), 13–22. [12] Rompotis, G. G. (2011). Predictable patterns in ETFs` return and tracking error. Studies in Economics and Finance. [13] Shin, S., & Soydemir, G. (2010). Exchange-traded funds, persistence in tracking errors and information dissemination. Journal of Multinational Financial Management, 20(4), 214–234. [14] Singh, J., & Kaur, P. (2016). Tracking efficiency of exchange traded funds (ETFs) empirical evidence from Indian equity ETFs. Paradigm, 20(2), 176-190. [15] 尤亭歡. (2014). 台灣, 香港, 中國三地 ETF追蹤誤差之研究. [16] 陳春鋒, & 陳偉忠. (2004). 指數優化復製的方法, 模型與實證. 數量經濟技術經濟研究, (12), 106-115.
Description:	碩士國立政治大學金融學系 109352036
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0109352036
Data Type:	thesis
DOI:	10.6814/NCCU202200632
Appears in Collections:	[Department of Money and Banking] Theses

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