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  • 學位論文

快速交易對波動率的影響:以台股期貨為例

The Influence of Fast Trading on Realized Volatility: Evidence from the Taiwan Stock Index Futures

指導教授 : 林蒼祥
共同指導教授 : 蔡蒔銓

摘要


本研究欲探討快速交易對波動率的影響,以台灣期貨市場中的台股期貨、小型台指期貨為樣本,當前台灣缺乏高頻交易的金融環境,受限於法規或機器設備的限制,因此根據Kirilenko (2017)研究中的條件,篩選出快速交易者,再依不同身分類別投資人進行快速交易量及快速交易比例的篩選,然Lin et al. (2018)研究指出,VIX指數25為分水嶺區分高低波動區間,將變數進行迴歸分析,探討快速交易與波動率兩者間的影響。 實證結果顯示,台股期貨與小型台指期貨的快速交易者在低波動期間,提供市場流動性,有降低波動率的功效,高波動區間,快速交易者則會提升市場波動率;本研究發現,研究樣本的散戶,為快速交易之散戶,較具有交易經驗,而小台又在技術投資者中受歡迎,顯示當市場劇烈波動時,部分提升了市場品質,也部分抑制了波動率。

並列摘要


This article would like to find out the influences between fast trading and the volatility in the Taiwan Stock Index Futures. Using the TAIEX Futures (TX) and the Mini-TAIEX Futures (MTX) as our research topic. Due to lack of the financial environment for high frequency trading, and have more obstruct from the low and mechanical equipment. This article based on Kirilenko (2017)’s study, filter out the fast trader. All the fast trader is combined with the foreign institutions, domestic dealers, and retail investors. Using this data to calculate the fast trading volume and fast trading proportion. According to Lin (2018)’s study, VIX 25 is the index to divide into two group, high volatility and low volatility. Regressing the variables and analyzing the influence of fast trading and volatility. The empirical results show that fast trader supports the market illiquidity and decline the volatility in the low volatility on TX and MTX. On the other hand, fast trader increases the volatility in the high volatility; this article filtering out the retail, the retail is more experience trader, and MTX is more popular among skilled investors. This article show that when the market in the high volatility, retail increase the market quality and reduce the volatility.

參考文獻


參考文獻
1.喬帥、鄭振龍、陳志英 (2018),期權市場老練散戶交易行為分析,管理科學學報,2018年10月接受。
2.Benos, E., & Sagade, S. (2012). High-frequency trading behaviour and its impact on market quality: Evidence from the UK equity market. Bank of England. Quarterly Bullentin, 52(4), 370.
3.Boehmer, E., Fong, K., & Wu, J. (2012, March). International evidence on algorithmic trading. In AFA 2013 San Diego Meetings Paper.
4.Brogaard, J. (2010). High frequency trading and its impact on market quality. Northwestern University Kellogg School of Management Working Paper, 66

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