High Frequency Trading, Liquidity, and Price Impact

2012 ◽  
Author(s):  
Edward W. Sun ◽  
Timm Kruse ◽  
Min-Teh Yu
Keyword(s):  
High Frequency ◽  
Price Impact ◽  
High Frequency Trading

Statistical characteristics of price impact in high-frequency trading

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Can Jia ◽  
Tianmin Zhou ◽  
Handong Li
Keyword(s):  
High Frequency ◽  
Trading Volume ◽  
Stock Exchange ◽  
Price Change ◽  
Price Impact ◽  
High Frequency Trading ◽  
Statistical Characteristics ◽  
Long Term Memory ◽  
Time Interval ◽  
Trade Volume

AbstractTrading volume changes based on market microstructure will impact asset prices, which will lead to transaction price changes. Based on the extended Hasbrouck–Foster–Viswanathan (HFV) model, we study the statistical characteristics of daily permanent price impact and daily temporary price impact using high-frequency data from Chinese Stock Markets. We estimate this model using tick-by-tick data for 16 selected stocks that are traded on the Shanghai Stock Exchange. We find the following: (1) the time series of both the permanent price impact and temporary price impact exist in stationarity and long-term memory; (2) there is a strong correlation between the permanent price impact among assets, while the correlation coefficient of the temporary price impact is generally weak; (3) the time interval has no significant influence on the trade volume and the price change at the tick frequency, which means that it is not necessary to take into account the time interval between adjacent transaction in high-frequency trading; and (4) the bid-ask spread is an effective factor to explain trading price change, but has no significant impact on trade volume.

scholarly journals Quantifying the High-Frequency Trading “Arms Race”

2021 ◽  
Author(s):  
Matteo Aquilina ◽  
Eric Budish ◽  
Peter O’Neill
Keyword(s):  
High Frequency ◽  
Trading Volume ◽  
Stock Exchange ◽  
Limit Order Book ◽  
Price Impact ◽  
High Frequency Trading ◽  
Order Book ◽  
Limit Order ◽  
The Cost ◽  
Latency Arbitrage

Abstract We use stock exchange message data to quantify the negative aspect of high-frequency trading, known as “latency arbitrage.” The key difference between message data and widely familiar limit order book data is that message data contain attempts to trade or cancel that fail. This allows the researcher to observe both winners and losers in a race, whereas in limit order book data you cannot see the losers, so you cannot directly see the races. We find that latency arbitrage races are very frequent (about one per minute per symbol for FTSE 100 stocks), extremely fast (the modal race lasts 5–10 millionths of a second), and account for a remarkably large portion of overall trading volume (about 20%). Race participation is concentrated, with the top six firms accounting for over 80% of all race wins and losses. The average race is worth just a small amount (about half a price tick), but because of the large volumes the stakes add up. Our main estimates suggest that races constitute roughly one-third of price impact and the effective spread (key microstructure measures of the cost of liquidity), that latency arbitrage imposes a roughly 0.5 basis point tax on trading, that market designs that eliminate latency arbitrage would reduce the market’s cost of liquidity by 17%, and that the total sums at stake are on the order of $5 billion per year in global equity markets alone.

scholarly journals Where is the Value in High Frequency Trading?

2012 ◽  
Vol 02 (03) ◽  
pp. 1250014 ◽  
Author(s):  
Álvaro Cartea ◽  
José Penalva
Keyword(s):  
Financial Markets ◽  
High Frequency ◽  
Market Price ◽  
Price Impact ◽  
High Frequency Trading ◽  
Microstructure Noise ◽  
Noise Trading ◽  
Surplus Extraction ◽  
The Impact ◽  
Professional Traders

We analyze the impact of high frequency (HF) trading in financial markets based on a model with three types of traders: liquidity traders (LTs), professional traders (PTs), and high frequency traders (HFTs). Our four main findings are: (i) The price impact of liquidity trades is higher in the presence of the HFTs and is increasing with the size of the trade. In particular, we show that HFTs reduce (increase) the prices that LTs receive when selling (buying) their equity holdings. (ii) Although PTs lose revenue in every trade intermediated by HFTs, they are compensated with a higher liquidity discount in the market price. (iii) HF trading increases the microstructure noise of prices. (iv) The volume of trades increases as the HFTs intermediate trades between the LTs and PTs. This additional volume is a consequence of trades which are carefully tailored for surplus extraction and are neither driven by fundamentals nor is it noise trading. In equilibrium, HF trading and PTs coexist as competition drives down the profits for new HFTs while the presence of HFTs does not drive out traditional PTs.

High-Frequency Financial Econometrics

2014 ◽  
Author(s):  
Yacine Aït-Sahalia ◽  
Jean Jacod
Keyword(s):  
High Frequency ◽  
Practical Importance ◽  
Financial Econometrics ◽  
Trading Strategies ◽  
Financial Data ◽  
Jump Processes ◽  
High Frequency Trading ◽  
Microstructure Noise ◽  
Econometric Methods ◽  
Mathematical Foundations

High-frequency trading is an algorithm-based computerized trading practice that allows firms to trade stocks in milliseconds. Over the last fifteen years, the use of statistical and econometric methods for analyzing high-frequency financial data has grown exponentially. This growth has been driven by the increasing availability of such data, the technological advancements that make high-frequency trading strategies possible, and the need of practitioners to analyze these data. This comprehensive book introduces readers to these emerging methods and tools of analysis. The book covers the mathematical foundations of stochastic processes, describes the primary characteristics of high-frequency financial data, and presents the asymptotic concepts that their analysis relies on. It also deals with estimation of the volatility portion of the model, including methods that are robust to market microstructure noise, and address estimation and testing questions involving the jump part of the model. As the book demonstrates, the practical importance and relevance of jumps in financial data are universally recognized, but only recently have econometric methods become available to rigorously analyze jump processes. The book approaches high-frequency econometrics with a distinct focus on the financial side of matters while maintaining technical rigor, which makes this book invaluable to researchers and practitioners alike.

High-Frequency Trading

2011 ◽  
Author(s):  
Peter Gomber ◽  
Björn Arndt ◽  
Marco Lutat ◽  
Tim Elko Uhle
Keyword(s):  
High Frequency ◽  
High Frequency Trading
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