High-Frequency Trading in the U.S. Treasury Market Around Macroeconomic News Announcements

Author(s):  
George Jiang ◽  
Ingrid Lo ◽  
Giorgio Valente
2021 ◽  
Author(s):  
◽  
Rui Qiao

<p>My thesis consists of three essays on market microstructure. Focusing on the U.S. Treasury market, I investigate several interesting research questions by using twelve years of BrokerTec order books of 2-, 5-, and 10-year on-the-run U.S. Treasury notes from January 1, 2004 to December 31, 2015, and five years of BrokerTec order books of 3-, 7- and 30-year on-the-run U.S. Treasury securities from January 1, 2011 to December 31, 2015. In the U.S. Treasury market, BrokerTec is one of the two dominant electronic communication networks (ECNs). According to my calculations by using BrokerTec order books from 2011 to 2015, the average daily trading volume of BrokerTec on-the-run U.S. Treasury securities is about 134.9 billion U.S. dollars, which accounts for about 26% of that of the total U.S. Treasury primary dealer activity. To help a wider audience better understand the importance of the research questions in the following three chapters, Chapter 1 gives a brief introduction to the U.S. Treasury market.  In Chapter 2, I investigate the impact of scheduled macroeconomic news announcements on the U.S. Treasury market efficiency. To control the microstructure noise, I employ a robust method to construct market inefficiency measures. I find that the U.S. Treasury market becomes less efficient starting from five minutes before news arrivals. The finding is robust for different sample periods, macroeconomic news announcements, and market inefficiency measures. Investor heterogeneity could explain the decreased market efficiency before scheduled news announcements.  In Chapter 3, I investigate the impact of workup trading protocols on the U.S. Treasury market quality. Each transaction on the lit pool opens a workup window, during which the BrokerTec trading platform continues to receive order submissions and modifications, but only matches workup orders that have the same prices. Each workup transaction starts a new counting down of the workup clock. A workup window naturally closes either after the workup times out or when a limit order is submitted at a better price. I find that the workup trading activities decrease the market quality, in aspects of market efficiency and market liquidity.  In Chapter 4, I empirically examine the role of heterogeneity in traders’ beliefs and public information shocks on traders’ order submission decisions around news announcements in the U.S. Treasury market. I find that during both the pre-announcement period and the post-announcement period, the traders tend to submit more market orders and aggressive limit orders when the market uncertainty is high. I also find that the belief heterogeneity influences investors’ trading behavior and order submission strategies around news announcements. The role of the belief heterogeneity on order aggressiveness depends on the type of news, and the magnitude of the information shocks. The impact of market uncertainty and belief heterogeneity influences traders’ submission of both of the market orders and aggressive limit orders.  In Chapter 5, I provide a summary on the research findings in Chapter 2, Chapter 3 and Chapter 4. I also discuss the contributions of this thesis to the literature.</p>


2021 ◽  
Author(s):  
◽  
Rui Qiao

<p>My thesis consists of three essays on market microstructure. Focusing on the U.S. Treasury market, I investigate several interesting research questions by using twelve years of BrokerTec order books of 2-, 5-, and 10-year on-the-run U.S. Treasury notes from January 1, 2004 to December 31, 2015, and five years of BrokerTec order books of 3-, 7- and 30-year on-the-run U.S. Treasury securities from January 1, 2011 to December 31, 2015. In the U.S. Treasury market, BrokerTec is one of the two dominant electronic communication networks (ECNs). According to my calculations by using BrokerTec order books from 2011 to 2015, the average daily trading volume of BrokerTec on-the-run U.S. Treasury securities is about 134.9 billion U.S. dollars, which accounts for about 26% of that of the total U.S. Treasury primary dealer activity. To help a wider audience better understand the importance of the research questions in the following three chapters, Chapter 1 gives a brief introduction to the U.S. Treasury market.  In Chapter 2, I investigate the impact of scheduled macroeconomic news announcements on the U.S. Treasury market efficiency. To control the microstructure noise, I employ a robust method to construct market inefficiency measures. I find that the U.S. Treasury market becomes less efficient starting from five minutes before news arrivals. The finding is robust for different sample periods, macroeconomic news announcements, and market inefficiency measures. Investor heterogeneity could explain the decreased market efficiency before scheduled news announcements.  In Chapter 3, I investigate the impact of workup trading protocols on the U.S. Treasury market quality. Each transaction on the lit pool opens a workup window, during which the BrokerTec trading platform continues to receive order submissions and modifications, but only matches workup orders that have the same prices. Each workup transaction starts a new counting down of the workup clock. A workup window naturally closes either after the workup times out or when a limit order is submitted at a better price. I find that the workup trading activities decrease the market quality, in aspects of market efficiency and market liquidity.  In Chapter 4, I empirically examine the role of heterogeneity in traders’ beliefs and public information shocks on traders’ order submission decisions around news announcements in the U.S. Treasury market. I find that during both the pre-announcement period and the post-announcement period, the traders tend to submit more market orders and aggressive limit orders when the market uncertainty is high. I also find that the belief heterogeneity influences investors’ trading behavior and order submission strategies around news announcements. The role of the belief heterogeneity on order aggressiveness depends on the type of news, and the magnitude of the information shocks. The impact of market uncertainty and belief heterogeneity influences traders’ submission of both of the market orders and aggressive limit orders.  In Chapter 5, I provide a summary on the research findings in Chapter 2, Chapter 3 and Chapter 4. I also discuss the contributions of this thesis to the literature.</p>


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.


Author(s):  
Peter Gomber ◽  
Björn Arndt ◽  
Marco Lutat ◽  
Tim Elko Uhle

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