This article introduces a high-frequency trade execution model to evaluate the economic impact of supervised machine learners. Extending the concept of a confusion matrix, we present a “trade information matrix” to attribute the expected profit and loss of the high-frequency strategy under execution constraints, such as fill probabilities and position dependent trade rules, to correct and incorrect predictions. We apply the trade execution model and trade information matrix to Level II E-mini S&P 500 futures history and demonstrate an estimation approach for measuring the sensitivity of the P&L to the error of a recurrent neural network. Our approach directly evaluates the performance sensitivity of a market-making strategy to prediction error and augments traditional market simulation-based testing.
{"title":"A high-frequency trade execution model for supervised learning†","authors":"Matthew Dixon","doi":"10.1002/hf2.10016","DOIUrl":"https://doi.org/10.1002/hf2.10016","url":null,"abstract":"<p>This article introduces a high-frequency trade execution model to evaluate the economic impact of supervised machine learners. Extending the concept of a confusion matrix, we present a “trade information matrix” to attribute the expected profit and loss of the high-frequency strategy under execution constraints, such as fill probabilities and position dependent trade rules, to correct and incorrect predictions. We apply the trade execution model and trade information matrix to Level II E-mini S&P 500 futures history and demonstrate an estimation approach for measuring the sensitivity of the P&L to the error of a recurrent neural network. Our approach directly evaluates the performance sensitivity of a market-making strategy to prediction error and augments traditional market simulation-based testing.</p>","PeriodicalId":100604,"journal":{"name":"High Frequency","volume":"1 1","pages":"32-52"},"PeriodicalIF":0.0,"publicationDate":"2018-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/hf2.10016","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"137975476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A novel approach to the analysis of S & P 500 market fluctuations is proposed using a K-component mixture of regressions model. The Barndorff-Nielsen and Shephard stochastic model is employed where the estimates of jumps of log-returns are governed by Lévy subordinators. Daily VIX and VIX2 close prices are analyzed as the indicators of log-return volatility and the corresponding variance of the S & P 500 index using the mixture model. The behavior of the S & P 500 market from 1 August 2005 to 31 December 2009 is analyzed and forecasted. A set of rules are provided to predict monthly fluctuation in the S & P 500 market. The procedure used in this paper gives a novel approach for constructing an “indicator”of non-Gaussian jump of an empirical data set in finance using mixture of regression (Gaussian) analysis.
{"title":"A new analysis of VIX using mixture of regressions: Examination and short-term forecasting for the S & P 500 market","authors":"Tatjana Miljkovic, Indranil SenGupta","doi":"10.1002/hf2.10009","DOIUrl":"10.1002/hf2.10009","url":null,"abstract":"<p>A novel approach to the analysis of S & P 500 market fluctuations is proposed using a K-component mixture of regressions model. The Barndorff-Nielsen and Shephard stochastic model is employed where the estimates of jumps of log-returns are governed by Lévy subordinators. Daily VIX and VIX<sup>2</sup> close prices are analyzed as the indicators of log-return volatility and the corresponding variance of the S & P 500 index using the mixture model. The behavior of the S & P 500 market from 1 August 2005 to 31 December 2009 is analyzed and forecasted. A set of rules are provided to predict monthly fluctuation in the S & P 500 market. The procedure used in this paper gives a novel approach for constructing an “indicator”of non-Gaussian jump of an empirical data set in finance using mixture of regression (Gaussian) analysis.</p>","PeriodicalId":100604,"journal":{"name":"High Frequency","volume":"1 1","pages":"53-65"},"PeriodicalIF":0.0,"publicationDate":"2018-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/hf2.10009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"104791645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mahayaudin M. Mansor, David A. Green, Andrew V. Metcalfe
We provide empirical evidence of directionality in high-frequency multivariate time series of the five largest U.S. banks between 1999 and 2017. The directionality is more apparent during crisis periods than during noncrisis periods, and it has only a low association with volatility. We use directionality and volatility as a regime-switching criterion between two-regime threshold vector autoregressive (TVAR) models for forecasting share prices. We compare the forecasting performances using mean relative error squared, and a weighted average of the forecasting error, with weights based on the estimated conditional variance, for individual model components and as a group. We have demonstrated that moving directionality can provide early warning of increased volatility and crisis periods, and has potential for improving one-step ahead forecasts using TVAR(1) models.
{"title":"Directionality and volatility in high-frequency time series","authors":"Mahayaudin M. Mansor, David A. Green, Andrew V. Metcalfe","doi":"10.1002/hf2.10008","DOIUrl":"10.1002/hf2.10008","url":null,"abstract":"<p>We provide empirical evidence of directionality in high-frequency multivariate time series of the five largest U.S. banks between 1999 and 2017. The directionality is more apparent during crisis periods than during noncrisis periods, and it has only a low association with volatility. We use directionality and volatility as a regime-switching criterion between two-regime threshold vector autoregressive (TVAR) models for forecasting share prices. We compare the forecasting performances using mean relative error squared, and a weighted average of the forecasting error, with weights based on the estimated conditional variance, for individual model components and as a group. We have demonstrated that moving directionality can provide early warning of increased volatility and crisis periods, and has potential for improving one-step ahead forecasts using TVAR(1) models.</p>","PeriodicalId":100604,"journal":{"name":"High Frequency","volume":"1 2","pages":"70-86"},"PeriodicalIF":0.0,"publicationDate":"2018-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/hf2.10008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"104405206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The literature on derivative securities evidently does not contain any empirical studies of liquidity and volatility with high-frequency data for leading Australian financial futures contracts, during the general financial crisis. Moreover, while the extant literature has studied the liquidity interdependence between spot markets for bonds and equities, the liquidity interdependence between the futures markets for these securities has been neglected. This article, in addressing these deficiencies, estimates the relationship between liquidity and realized volatility, for Australian 90 Day Bank Accepted Bills (BAB) futures (first interest rate futures contract outside USA), with high-frequency data from the crisis year 2008, and studies the effect of changes in liquidity of S&P200 Index futures on the liquidity of BAB futures. The main results are first, the key variables are positively skewed and leptokurtic. Second, the ask–bid spread for BAB futures varies directly with volatility, which is consistent with a private information interpretation of volatility, which increases asymmetric information costs. Third, the ask–bid spreads for BAB futures and S&P200 futures exhibit positive covariation, which is consistent with the view of commonality in liquidity. The results of this research will be relevant to policymakers, regulators, market participants, educators, and graduate students.
{"title":"Liquidity and realized volatility in short-term interest rate futures in 2008 crisis","authors":"Barry A. Goss, S. Gulay Avsar","doi":"10.1002/hf2.10007","DOIUrl":"10.1002/hf2.10007","url":null,"abstract":"<div>\u0000 \u0000 <p>The literature on derivative securities evidently does not contain any empirical studies of liquidity and volatility with high-frequency data for leading Australian financial futures contracts, during the general financial crisis. Moreover, while the extant literature has studied the liquidity interdependence between spot markets for bonds and equities, the liquidity interdependence between the futures markets for these securities has been neglected. This article, in addressing these deficiencies, estimates the relationship between liquidity and realized volatility, for Australian 90 Day Bank Accepted Bills (BAB) futures (first interest rate futures contract outside USA), with high-frequency data from the crisis year 2008, and studies the effect of changes in liquidity of S&P200 Index futures on the liquidity of BAB futures. The main results are first, the key variables are positively skewed and leptokurtic. Second, the ask–bid spread for BAB futures varies directly with volatility, which is consistent with a private information interpretation of volatility, which increases asymmetric information costs. Third, the ask–bid spreads for BAB futures and S&P200 futures exhibit positive covariation, which is consistent with the view of commonality in liquidity. The results of this research will be relevant to policymakers, regulators, market participants, educators, and graduate students.</p>\u0000 </div>","PeriodicalId":100604,"journal":{"name":"High Frequency","volume":"1 1","pages":"21-31"},"PeriodicalIF":0.0,"publicationDate":"2017-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/hf2.10007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"108964007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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