Pub Date : 2018-07-31DOI: 10.3905/JOT.2018.13.3.005
Harald Carlens, D. Higgins
This article examines the impact of MiFID II on European equity market liquidity. MiFID II eliminated broker crossing networks, introduced caps on dark trading, and brought about new types of venues. The authors investigate the changes in the market in the lead-up to the January 3, 2018, implementation date and the early evidence supporting the expected liquidity shift toward block networks, periodic auctions, and systematic internalizers. Although all signs indicate limited change for end-investors, the delay in implementation of the double-volume caps means it is too early to fully assess the impact on trading costs.
{"title":"MiFID II and Equity Market Liquidity, or There and Back Again","authors":"Harald Carlens, D. Higgins","doi":"10.3905/JOT.2018.13.3.005","DOIUrl":"https://doi.org/10.3905/JOT.2018.13.3.005","url":null,"abstract":"This article examines the impact of MiFID II on European equity market liquidity. MiFID II eliminated broker crossing networks, introduced caps on dark trading, and brought about new types of venues. The authors investigate the changes in the market in the lead-up to the January 3, 2018, implementation date and the early evidence supporting the expected liquidity shift toward block networks, periodic auctions, and systematic internalizers. Although all signs indicate limited change for end-investors, the delay in implementation of the double-volume caps means it is too early to fully assess the impact on trading costs.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129335966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-07-31DOI: 10.3905/jot.2018.13.3.001
Brian R. Bruce
Dave BliDe Publisher The Summer issue of the journal begins with an examination by Carlens and Higgins of the impact of MiFID II on European equity market liquidity. They investigate the changes in the market in the lead-up to the January 3, 2018 implementation date and the early evidence supporting the expected liquidity shift toward block networks, periodic auctions, and systematic internalizers. In August 2012, the New York Stock Exchange (NYSE) launched the Retail Liquidity Program (RLP). The RLP enables market makers to quote dark (nondisplayed) limit orders that can be filled only by market orders that originate from retail traders. Garriott and Walton study the informational and market-quality impacts of segmentation using Trade and Quote (TAQ) data from the NYSE. They analyze the mechanism by which segmentation affects market quality by computing the information share of each component of the order f low using the techniques of Hasbrouck (The Journal of Finance, 1991). Next, Cole, Van Ness, and Van Ness study municipal bond market activity before, during, and after natural disasters (tornadoes, wildfires, and hurricanes/tropical storms). Using a sample of municipal bond trades from 2010 to 2013, they find that natural disasters inf luence municipal bond trading. They also determine that linkages exist between the bonds affected by natural disasters and related bonds. To continue, Kakushadze and Yu provide an explicit formulaic algorithm and source code for building long-only benchmark portfolios and then using these benchmarks in long-only market outperformance strategies. They use a multifactor risk model (which utilizes multilevel industry classification or clustering) specifically tailored to long-only benchmark portfolios to compute their weights, which are explicitly positive in their construction. To conclude this issue, Graf Plessen and Bemporad present a simple method for a posteriori (historical) multivariate, multistage optimal trading under transaction costs and a diversification constraint. The developed methods are based on efficient graph generation and consequent graph search and are evaluated quantitatively on real-world data. The fundamental motivation of this work is preparatory labeling of financial time-series data for supervised machine learning. As always, we welcome your submissions. We value your comments and suggestions, so please email us at journals@investmentresearch.org.
该杂志的夏季期以Carlens和Higgins对MiFID II对欧洲股票市场流动性的影响的研究开始。他们调查了2018年1月3日实施日期之前市场的变化，以及支持预期流动性向区块网络、定期拍卖和系统内部化转移的早期证据。2012年8月，纽约证券交易所(NYSE)推出了零售流动性计划(RLP)。RLP使做市商能够报价黑暗(未显示)限价单，这些限价单只能由来自零售交易商的市场订单来完成。Garriott和Walton利用纽约证券交易所的交易和报价(TAQ)数据研究了细分对信息和市场质量的影响。他们利用Hasbrouck (the Journal of Finance, 1991)的技术，通过计算订单流中每个组成部分的信息份额，分析了细分影响市场质量的机制。接下来，科尔、范内斯和范内斯研究了自然灾害(龙卷风、野火和飓风/热带风暴)发生之前、发生期间和发生之后的市政债券市场活动。利用2010 - 2013年的市政债券交易样本，他们发现自然灾害会影响市政债券交易。它们还确定受自然灾害影响的债券与相关债券之间存在联系。为了继续，Kakushadze和Yu提供了一个明确的公式算法和源代码，用于构建只做多的基准投资组合，然后在只做多的市场跑赢策略中使用这些基准。他们使用一个多因素风险模型(利用多层次的行业分类或聚类)，专门为只做多的基准投资组合量身定制，以计算它们的权重，这些权重在它们的结构中是明确的正的。为了总结这一问题，Graf Plessen和Bemporad提出了一种简单的方法，用于交易成本和多样化约束下的后先验(历史)多变量多阶段最优交易。所开发的方法基于高效的图生成和后续图搜索，并在实际数据上进行了定量评估。这项工作的基本动机是为监督机器学习准备金融时间序列数据的标记。一如既往，我们欢迎您的投稿。我们非常重视您的意见和建议，所以请给我们发邮件至journals@investmentresearch.org。
{"title":"Editor’s Letter","authors":"Brian R. Bruce","doi":"10.3905/jot.2018.13.3.001","DOIUrl":"https://doi.org/10.3905/jot.2018.13.3.001","url":null,"abstract":"Dave BliDe Publisher The Summer issue of the journal begins with an examination by Carlens and Higgins of the impact of MiFID II on European equity market liquidity. They investigate the changes in the market in the lead-up to the January 3, 2018 implementation date and the early evidence supporting the expected liquidity shift toward block networks, periodic auctions, and systematic internalizers. In August 2012, the New York Stock Exchange (NYSE) launched the Retail Liquidity Program (RLP). The RLP enables market makers to quote dark (nondisplayed) limit orders that can be filled only by market orders that originate from retail traders. Garriott and Walton study the informational and market-quality impacts of segmentation using Trade and Quote (TAQ) data from the NYSE. They analyze the mechanism by which segmentation affects market quality by computing the information share of each component of the order f low using the techniques of Hasbrouck (The Journal of Finance, 1991). Next, Cole, Van Ness, and Van Ness study municipal bond market activity before, during, and after natural disasters (tornadoes, wildfires, and hurricanes/tropical storms). Using a sample of municipal bond trades from 2010 to 2013, they find that natural disasters inf luence municipal bond trading. They also determine that linkages exist between the bonds affected by natural disasters and related bonds. To continue, Kakushadze and Yu provide an explicit formulaic algorithm and source code for building long-only benchmark portfolios and then using these benchmarks in long-only market outperformance strategies. They use a multifactor risk model (which utilizes multilevel industry classification or clustering) specifically tailored to long-only benchmark portfolios to compute their weights, which are explicitly positive in their construction. To conclude this issue, Graf Plessen and Bemporad present a simple method for a posteriori (historical) multivariate, multistage optimal trading under transaction costs and a diversification constraint. The developed methods are based on efficient graph generation and consequent graph search and are evaluated quantitatively on real-world data. The fundamental motivation of this work is preparatory labeling of financial time-series data for supervised machine learning. As always, we welcome your submissions. We value your comments and suggestions, so please email us at journals@investmentresearch.org.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126604810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-21DOI: 10.3905/jot.2018.13.3.013
Corey Garriott, Adrian Walton
In August 2012, the New York Stock Exchange launched the Retail Liquidity Program (RLP), a new trading facility that enables participating organizations to quote dark limit orders available only to retail traders. The facility increased the information content of the order flow by distinguishing retail trades from relatively more informed trades. Stocks with substantial RLP activity experienced no material changes in relative bid–ask spreads, effective spreads, and price impacts, and had mildly decreased return autocorrelations.
{"title":"Retail Order Flow Segmentation","authors":"Corey Garriott, Adrian Walton","doi":"10.3905/jot.2018.13.3.013","DOIUrl":"https://doi.org/10.3905/jot.2018.13.3.013","url":null,"abstract":"In August 2012, the New York Stock Exchange launched the Retail Liquidity Program (RLP), a new trading facility that enables participating organizations to quote dark limit orders available only to retail traders. The facility increased the information content of the order flow by distinguishing retail trades from relatively more informed trades. Stocks with substantial RLP activity experienced no material changes in relative bid–ask spreads, effective spreads, and price impacts, and had mildly decreased return autocorrelations.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130950170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The authors study municipal bond market activity before, during, and after natural disasters (tornadoes, wildfires, and hurricanes/tropical storms). Using a sample of municipal bond trades from 2010 to 2013, they find that natural disasters influence municipal bond trading. Specifically, they show that spreads are lower on both tornado and wildfire event days and during the following five trading days than during the preceding five trading days. Although the study does not document a relationship between hurricane events and spreads, the authors show that spreads fall during the five days following the hurricane compared with the five trading days before the event. Generally, the study shows an increase in dollar volume in the five trading days following all three types of natural disasters. The authors also find linkages between the bonds affected by natural disasters and related bonds.
{"title":"Municipal Bond Trading, Information Relatedness, and Natural Disasters","authors":"Brittany Cole, Bonnie F. Van Ness, R. V. Ness","doi":"10.3905/jot.2018.1.063","DOIUrl":"https://doi.org/10.3905/jot.2018.1.063","url":null,"abstract":"The authors study municipal bond market activity before, during, and after natural disasters (tornadoes, wildfires, and hurricanes/tropical storms). Using a sample of municipal bond trades from 2010 to 2013, they find that natural disasters influence municipal bond trading. Specifically, they show that spreads are lower on both tornado and wildfire event days and during the following five trading days than during the preceding five trading days. Although the study does not document a relationship between hurricane events and spreads, the authors show that spreads fall during the five days following the hurricane compared with the five trading days before the event. Generally, the study shows an increase in dollar volume in the five trading days following all three types of natural disasters. The authors also find linkages between the bonds affected by natural disasters and related bonds.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126041803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article provides an explicit formulaic algorithm and source code for building long-only benchmark portfolios and then using these benchmarks in long-only market outperformance strategies. The benchmarks (or the corresponding betas) do not involve any principal components, nor do they require iterations. Instead, the authors use a multifactor risk model (which uses multilevel industry classification or clustering) specifically tailored to long-only benchmark portfolios to compute their weights, which are explicitly positive in the construction.
{"title":"Betas, Benchmarks, and Beating the Market","authors":"Zurab Kakushadze, Willie Yu","doi":"10.2139/ssrn.3187779","DOIUrl":"https://doi.org/10.2139/ssrn.3187779","url":null,"abstract":"This article provides an explicit formulaic algorithm and source code for building long-only benchmark portfolios and then using these benchmarks in long-only market outperformance strategies. The benchmarks (or the corresponding betas) do not involve any principal components, nor do they require iterations. Instead, the authors use a multifactor risk model (which uses multilevel industry classification or clustering) specifically tailored to long-only benchmark portfolios to compute their weights, which are explicitly positive in the construction.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129966249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-03-10DOI: 10.3905/jot.2018.13.2.047
D. Buehler, Patrick Cusatis
This article examines the use of exchange-traded funds (ETFs) in the implied volatility market. Because the Volatility Index (VIX) cannot be directly traded and the VIX futures market is accessible only to institutional investors, the authors develop and analyze how individual investors can employ a VIX-based strategy using ETFs. They test a trading strategy using the ProShares VIXY and SVXY ETFs and compare the performance to a similar strategy using VIX futures and the S&P 500. They select these two ETFs because they can directly compare a long or short trading strategy using VIX futures. While the ETF trading strategies produce excess returns, these returns come with significant downside volatility.
{"title":"Trading the VIX Futures Roll Using Exchange-Traded Funds","authors":"D. Buehler, Patrick Cusatis","doi":"10.3905/jot.2018.13.2.047","DOIUrl":"https://doi.org/10.3905/jot.2018.13.2.047","url":null,"abstract":"This article examines the use of exchange-traded funds (ETFs) in the implied volatility market. Because the Volatility Index (VIX) cannot be directly traded and the VIX futures market is accessible only to institutional investors, the authors develop and analyze how individual investors can employ a VIX-based strategy using ETFs. They test a trading strategy using the ProShares VIXY and SVXY ETFs and compare the performance to a similar strategy using VIX futures and the S&P 500. They select these two ETFs because they can directly compare a long or short trading strategy using VIX futures. While the ETF trading strategies produce excess returns, these returns come with significant downside volatility.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123276842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article presents a simple method for a posteriori (historical) multivariate, multistage optimal trading under transaction costs and a diversification constraint. Starting from a given amount of money in some currency, the authors analyze the stage-wise optimal allocation over a time horizon with potential investments in multiple currencies and various assets. Three variants are discussed: unconstrained trading frequency, a fixed number of total admissible trades, and waiting a specific time period after every executed trade until the next trade. The developed methods are based on efficient graph generation and consequent graph search and are evaluated quantitatively on real-world data. The fundamental motivation of this work is preparatory labeling of financial time-series data for supervised machine learning.
{"title":"A Posteriori Multistage Optimal Trading under Transaction Costs and a Diversification Constraint","authors":"Mogens Graf Plessen, A. Bemporad","doi":"10.3905/jot.2018.1.064","DOIUrl":"https://doi.org/10.3905/jot.2018.1.064","url":null,"abstract":"This article presents a simple method for a posteriori (historical) multivariate, multistage optimal trading under transaction costs and a diversification constraint. Starting from a given amount of money in some currency, the authors analyze the stage-wise optimal allocation over a time horizon with potential investments in multiple currencies and various assets. Three variants are discussed: unconstrained trading frequency, a fixed number of total admissible trades, and waiting a specific time period after every executed trade until the next trade. The developed methods are based on efficient graph generation and consequent graph search and are evaluated quantitatively on real-world data. The fundamental motivation of this work is preparatory labeling of financial time-series data for supervised machine learning.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129133825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-03-31DOI: 10.3905/jot.2017.12.2.073
Yu-Jung L. Avis, Chingfu Chang, Dandan Wu
We take the perspective of the practitioner who focuses on following the longitudinal performance of specific stocks and investigate whether volume may provide guidance on days of extreme price movements. For days of extreme price increases (the winners) and extreme price decreases (the losers), we show that extreme low volume is associated with future return reversal, whereas extreme high volume does not necessarily lead to future return persistence. We look at daily data from 1989 to 2014, and we consider 2004 to be the year when algorithmic trading activities began to intensify. We find that the usefulness of extreme low volume in repudiating extreme price movements has been diminishing since 2004. To the extent that extreme low volume may still be applied to repudiate extreme price movements, a practitioner may limit his or her scope to the low-volume winners and losers of small capitalization. In addition, we use Chinese data from 1992 to 2014 to replicate the tests. We find that the same characteristics are not shown there, indicating a lack of universality of the conclusions we derived from the U.S. data.
{"title":"Can Trading Volume Validate Extreme Price Movements in the Age of Higher Algorithmic Trading Activities?","authors":"Yu-Jung L. Avis, Chingfu Chang, Dandan Wu","doi":"10.3905/jot.2017.12.2.073","DOIUrl":"https://doi.org/10.3905/jot.2017.12.2.073","url":null,"abstract":"We take the perspective of the practitioner who focuses on following the longitudinal performance of specific stocks and investigate whether volume may provide guidance on days of extreme price movements. For days of extreme price increases (the winners) and extreme price decreases (the losers), we show that extreme low volume is associated with future return reversal, whereas extreme high volume does not necessarily lead to future return persistence. We look at daily data from 1989 to 2014, and we consider 2004 to be the year when algorithmic trading activities began to intensify. We find that the usefulness of extreme low volume in repudiating extreme price movements has been diminishing since 2004. To the extent that extreme low volume may still be applied to repudiate extreme price movements, a practitioner may limit his or her scope to the low-volume winners and losers of small capitalization. In addition, we use Chinese data from 1992 to 2014 to replicate the tests. We find that the same characteristics are not shown there, indicating a lack of universality of the conclusions we derived from the U.S. data.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129272933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-03-31DOI: 10.3905/jot.2017.12.2.028
V. Markov, Olga Vilenskaia, Vlad Rashkovich
We present a set of models that are relevant for predicting various aspects of intraday trading volume for equities and showcase them as an ensemble that projects volume in unison. We introduce econometric methods for predicting end-of-day volume, volume u-curve, close auction volume, and special day seasonalities and emphasize a need for a unified approach in which all submodels work consistently with each other. We rely on the application of Bayesian methods, which have the advantage of providing adaptive and parameterless estimations of volume for a broad range of equities while automatically taking into account uncertainty in the model input components. We discuss the shortcomings of traditional statistical error metrics for calibrating volume prediction and introduce asymmetrical logarithmic error to overweight an overestimation risk.
{"title":"Quintet Volume Projection","authors":"V. Markov, Olga Vilenskaia, Vlad Rashkovich","doi":"10.3905/jot.2017.12.2.028","DOIUrl":"https://doi.org/10.3905/jot.2017.12.2.028","url":null,"abstract":"We present a set of models that are relevant for predicting various aspects of intraday trading volume for equities and showcase them as an ensemble that projects volume in unison. We introduce econometric methods for predicting end-of-day volume, volume u-curve, close auction volume, and special day seasonalities and emphasize a need for a unified approach in which all submodels work consistently with each other. We rely on the application of Bayesian methods, which have the advantage of providing adaptive and parameterless estimations of volume for a broad range of equities while automatically taking into account uncertainty in the model input components. We discuss the shortcomings of traditional statistical error metrics for calibrating volume prediction and introduce asymmetrical logarithmic error to overweight an overestimation risk.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115304408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-03-31DOI: 10.3905/jot.2017.12.2.059
A. Oztekin, Suchi Mishra, P. Jain, R. Daigler, Sascha Strobl, R. Holowczak
Using high-frequency datasets, we examine price discovery and its determinants for equivalent instruments across futures markets, electronically traded exchange-traded funds (ETFs), and spot markets. We compare futures to ETFs—leveraged and unleveraged—for stock indexes, using both a normal period and the 2008 financial crisis. Yan and Zivot’s information leadership procedure is employed to determine which instrument dominates price discovery. We then examine the determinants and characteristics of the price discovery process using Hasbrouck’s sequential trading model for the price impact of large trades. We find that most price discovery occurs in the more liquid and highly leveraged futures market. Although liquidity declined in all markets during the financial crisis, the relative contribution of ETFs to price discovery increased. We also find that the information leadership shares of futures and ETFs depend on the ratio of the quoted percentage spread between futures and ETFs and the aggregate volatility occurring in these markets.
{"title":"Price Discovery and Liquidity Characteristics for U.S. Electronic Futures and ETF Markets","authors":"A. Oztekin, Suchi Mishra, P. Jain, R. Daigler, Sascha Strobl, R. Holowczak","doi":"10.3905/jot.2017.12.2.059","DOIUrl":"https://doi.org/10.3905/jot.2017.12.2.059","url":null,"abstract":"Using high-frequency datasets, we examine price discovery and its determinants for equivalent instruments across futures markets, electronically traded exchange-traded funds (ETFs), and spot markets. We compare futures to ETFs—leveraged and unleveraged—for stock indexes, using both a normal period and the 2008 financial crisis. Yan and Zivot’s information leadership procedure is employed to determine which instrument dominates price discovery. We then examine the determinants and characteristics of the price discovery process using Hasbrouck’s sequential trading model for the price impact of large trades. We find that most price discovery occurs in the more liquid and highly leveraged futures market. Although liquidity declined in all markets during the financial crisis, the relative contribution of ETFs to price discovery increased. We also find that the information leadership shares of futures and ETFs depend on the ratio of the quoted percentage spread between futures and ETFs and the aggregate volatility occurring in these markets.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114321339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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