scholarly journals Options Trading Costs Are Lower than You Think

2020 ◽  
Vol 33 (11) ◽  
pp. 4973-5014 ◽  
Author(s):  
Dmitriy Muravyev ◽  
Neil D Pearson

Abstract Conventional estimates of the costs of taking liquidity in options markets are large. Nonetheless, options trading volume is high. We resolve this puzzle by showing that options price changes are predictable at high frequency, and many traders time executions by buying (selling) when the option fair value is close to the ask (bid). Effective spreads of traders who time executions are less than 40% of the size of conventional measures, and the overall average effective spread is one-quarter smaller than conventional estimates. Price impact measures are also affected. These findings alter conclusions about the after-cost profitability of options trading strategies.

2019 ◽  
Vol 1 (1) ◽  
pp. 41-54
Author(s):  
Erman Denny Arfianto ◽  
Ivan Irawan

Purpose- This study aims to examine the effect of effective spread, price impact, trading volume, stock prices, and volatility of returns on the predictability of short-term returns (short horizon return predictability). Methods- This research offers a new approach perspective which is a market microstructure with intraday data to measure short horizon return predictability as an efficient market inversion. The sample in this study was 64 non-financial companies listed on the KOMPAS100 Index during October 2017-March 2018. Intraday data used using the 5-minute frequency obtained from Bloomberg. This study uses multiple linear regression analysis. Finding- This study found that price impact, trading volume, stock prices, and volatility have a positive impact on the predictability of long-term returns. This study also found that effective spread does not have a significant impact on the predictability of short-term returns.


2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Can Jia ◽  
Tianmin Zhou ◽  
Handong Li

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.


Economies ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 67 ◽  
Author(s):  
Hee-Joon Ahn ◽  
Jun Cai ◽  
Cheol-Won Yang

This study empirically investigates the low-frequency liquidity proxies that best measure liquidity in emerging markets. We carry out a comprehensive analysis using tick data that cover 1183 stocks from 21 emerging markets, while also comparing various low-frequency liquidity proxies with high-frequency spread measures and price impact measures. We find that the Lesmond, Ogden, and Trzcinka (LOT) measure is the most effective spread proxy in most emerging markets. Among the price impact proxies, the Amihud measure is the most effective.


Author(s):  
Matteo Aquilina ◽  
Eric Budish ◽  
Peter O’Neill

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.


Author(s):  
Yacine Aït-Sahalia ◽  
Jean Jacod

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.


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