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。

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.

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