演算法交易行為對外匯市場質量之影響

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：18

、訪客IP：3.16.70.101

姓名

張碧瑜(Biyu Zhang) 查詢紙本館藏

畢業系所

財務金融學系

論文名稱

演算法交易行為對外匯市場質量之影響

相關論文

★ 從巴塞爾協定三談商業銀行資金流動性穩健指標	★ 三大法人於台灣期貨市場擇時能力之探討
★ 奢侈稅課徵對於台灣房價之影響	★ 外匯曝險對台灣半導體產業之現金流量的影響
★ 金控法規範的利害關係人非授信交易之探討	★ 歐債危機是否會影響台灣股市?以台灣指數股票型基金為例
★ 寬鬆貨幣政策對於歐元匯率的影響	★ 影響境外人民幣和境內人民幣價差變化的因素
★ 台灣銀行業高階經理人薪酬與銀行特性之關連性分析	★ 承銷業務對證券分析師盈餘預測之影響
★ 經紀業務對分析師盈餘預測影響	★ 領導者或追隨者：被忽略公司分析師盈餘預測行為之研究
★ 個別投資人日內交易損益:臺灣期貨市場實證分析	★ 外匯市場私有訊息之程度對於匯率變動之影響
★ 外國機構投資人和外匯市場：以臺北外匯交易市場為例	★ 散戶與三大法人之處份效果研究:以台灣加權股價指數期貨為例

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

隨著演算法交易（Algorithmic trading）在市場上所佔的比重越來越大，演算法交易行為必然會對金融市場產生一定的影響。演算法交易相關研究通常可劃分為演算法交易本身的交易策略和演算法交易對市場的影響兩大主要方面。傳統文獻已經有很多關注演算法交易對股票市場之影響的研究，如Boehmer, Fong and Wu(2014)研究全球42個股票市場上從2001到2011年演算法交易對流動性、短期波動、訊息效率性的強度影響。因此本文把研究的方向轉向外匯市場。本文利用 EBS(Electronic Broking Services)外匯交易系統的歐元兌美元和日元兌美元的交易資料，採用向量自我回歸（vector autoregression，VAR）模型來分析演算法交易行為對外匯市場質量的影響。結果發現：在歐元兌美元和日元兌美元這兩種最主流的外匯交易中，演算法交易行為會提升流動性、增加波動、對訊息效率性并沒有顯著影響。

摘要(英)

As the algorithmic trading has continued to increased its share on the financial market, it must have an impact on the market. Related studies of algorithmic trading focuses on two themes: the strategy of algorithmic trading and its impact on market. Regarding the impact of algorithmic trading, Boehmer, Fong and Wu(2014), studied the effect of algorithmic trading intensity on equity market liquidity, short-term volatility, and informational efficiency in 42 equity markets around the world between 2001 and 2011. This thesis extends to study to the foreign exchange market. Using the data of Euro against US dollar and yen against US dollar from EBS we apply the vector autoregression(VAR) model to analyse the impact of algorithmic trading on the foreign exchange market. The empirical results shows that algorithmic trading improves the liquidity, increases the volatility, and has no significant effect on informational efficiency.

關鍵字(中)

★ 演算法交易
★ 外匯市場
★ VAR

關鍵字(英)

論文目次

第一章、緒論 1
第一節、研究背景 1
第二節、研究架構 5
第二章、文獻回顧 6
第一節、演算法交易本身的類型及績效評估 6
第二節、演算法交易行為對市場產生的影響 7
第三章、研究方法 15
第一節、變數設定 15
第二節、模型設定 18
第四章、實證結果 20
第一節、數據來源 20
第二節、描述性統計 20
第三節、VAR 回歸結果分析 21
第四節、衝擊反應函數分析 22
第五章、結論與建議 24
第一節、結論 24
第二節、研究限制與建議 24
參考文獻 26

參考文獻

Aitken, M., Cumming, D. J., and Zhan, F. ,2012. Identifying international start dates for algorithmic trading and high frequency trading. Available at SSRN 2172455.
Almgren, R., Lorenz, J. ,2007. Adaptive arrival price. Trading, 2007(1), 59-66.
Baker, G., and Tiwari, S. ,2004. Algorithmic Trading: Perceptions and Challenges. Working Paper.
Berke, L. J. ,2010. US Institutional Equity Brokerage 2010: Assets, Commission Management and Concentration. Research report, TABB Group.
Boehmer, E., Fong, K. Y., and Wu, J. J. ,2012. Algorithmic trading and changes in firms’ equity capital. FIRN Research Paper.
Boehmer, E., Fong, K. Y., and Wu, J. J. ,2014. International evidence on algorithmic trading. In AFA 2013 San Diego Meetings Paper.
Brogaard, J., Hendershott, T., and Riordan, R. ,2014. High-frequency trading and price discovery. Review of Financial Studies, 27(8), 2267-2306.
Cartea, A., and Penalva, J. ,2011. Where is the value in high frequency trading? SSRN eLibrary. forthcoming Quarterly Journal of Finance, http://ssrn. com/paper, 1712765.
Chaboud, A. P., Chiquoine, B., Hjalmarsson, E., and Vega, C. ,2014. Rise of the machines: Algorithmic trading in the foreign exchange market. The Journal of Finance, 69(5), 2045-2084.
Chlistalla, M., Speyer, B., Kaiser, S., and Mayer, T. ,2011. High-frequency trading. Deutsche Bank Research, 7.
Domowitz, I., Yegerman, H. 2005. The cost of algorithmic trading: a first look at comparative performance. Trading, 2005(1), 30-40.
Foucault, T., Menkveld, A. J., 2008. Competition for order flow and smart order routing systems. The Journal of Finance, 63(1), 119-158.
Gsell, M. ,2006. Is Algorithmic Trading Distinctively Different? Assessing its Behavior in Comparison to Informed, Momentum and Noise Traders.Assessing its Behavior in Comparison to Informed, Momentum and Noise Traders (November 28, 2006).
Gsell, M. ,2008. Assessing the impact of Algorithmic Trading on markets: A simulation approach (No. 2008/49). CFS Working Paper.
Hasbrouck, J. and Saar, G., 2011. Low-Latency Trading. The Journal of Financial Markets, 16(2013), 646-679.
Hendershott, T., Jones, C. M., Menkveld, A. J., 2011. Does algorithmic trading improve liquidity?. The Journal of Finance, 66(1), 1-33.
Hendershott, T., Riordan, R. ,2009. Algorithmic trading and information.Manuscript, University of California, Berkeley.
Jarrow, R. A., and Protter, P. ,2011. Foreign currency bubbles. Review of Derivatives Research, 14(1), 67-83.
Kirilenko, A. A., Kyle, A. S., Samadi, M., and Tuzun, T. ,2014. The flash crash: The impact of high frequency trading on an electronic market.Available at SSRN 1686004.
Kissell, R. ,2007. Statistical Methods to Compare Algorithmic Performance.The Journal of Trading, 2(2), 53-62.
Kissell, R. ,2013. The science of algorithmic trading and portfolio management. Academic Press.
Konishi, Hizuru, 2002. Optimal slice of a VWAP trade. Journal of Financial Markets, 5(2), 197-221.
Menkveld, A. J., Jovanovic, B., 2010. Middlemen in Limit Order Markets. In 2010 Meeting Papers (No. 955). Society for Economic Dynamics.
Morris, D., Kantor-Hendrick, L. 2005. Key Considerations in Selecting an Algorithmic Trading Provider. Trading, 2005(1), 20-28.
Paskelian, O. G. ,2010. The impact of algorithmic trading models on the stock market. TRADING, 275.
Rime, D., and Schrimpf, A., 2013. The anatomy of the global FX market through the lens of the 2013 Triennial Survey. BIS Quarterly Review, December.
Rosenthal, D. W. ,2009. Performance metrics for algorithmic traders. UIC College of Business Administration Research Paper, (09-14).
Schmitz, J. E. ,2011. Algorithmic trading in the Iowa electronic markets.Algorithmic Finance, 1(2), 157-181.
Scholtus, M., van Dijk, D., and Frijns, B. ,2014. Speed, algorithmic trading, and market quality around macroeconomic news announcements. Journal of Banking & Finance, 38, 89-105.
Schwartz, R. A., Francioni, R., Weber, B. W. ,2006. Decision making in equity trading: Using simulation to get a grip. The Journal of Trading, 1(1), 59-74.
Sims, C. A. ,1980. Macroeconomics and reality. Econometrica: Journal of the Econometric Society, 1-48.
Viljoen, T., Westerholm, P. J., and Zheng, H. ,2014. Algorithmic trading, liquidity, and price discovery: An intraday analysis of the SPI 200 futures.Financial Review, 49(2), 245-270.
Yang, J., Jiu, B. ,2006. Algorithm selection: A quantitative approach.Trading, 2006(1), 26-34.
Zhang, F. ,2010. High-frequency trading, stock volatility, and price discovery. Available at SSRN 1691679.
陳旭昇. 2007. 時間序列分析: 總體經濟與財務金融之應用. 臺灣東華.

指導教授

高櫻芬

審核日期

2015-6-22

推文