HighFrequencyCovariance: A Julia package for estimating covariance matrices using high frequency financial data

High frequency data typically exhibit asynchronous trading and microstructure noise, which can bias the covariances estimated by standard estimators. While a number of specialized estimators have been proposed, they have had limited availability in open source software. HighFrequencyCovariance is th...

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Main Authors: Baumann, S, Klymak, M
Format: Journal article
Language:English
Published: Foundation for Open Access Statistics 2022
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author Baumann, S
Klymak, M
author_facet Baumann, S
Klymak, M
author_sort Baumann, S
collection OXFORD
description High frequency data typically exhibit asynchronous trading and microstructure noise, which can bias the covariances estimated by standard estimators. While a number of specialized estimators have been proposed, they have had limited availability in open source software. HighFrequencyCovariance is the first Julia package which implements specialized estimators for volatility, correlation and covariance using high frequency financial data. It also implements complementary algorithms for matrix regularization. This paper presents the issues associated with exploiting high frequency financial data and describes the volatility, covariance and regularization algorithms that have been implemented. We then demonstrate the use of the package using foreign exchange market tick data to estimate the covariance of the exchange rates between different currencies. We also perform a Monte Carlo experiment, which shows the accuracy gains that are possible over simpler covariance estimation techniques.
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spelling oxford-uuid:30b9964e-fe95-4759-93e8-324de445fb332022-09-20T15:38:58ZHighFrequencyCovariance: A Julia package for estimating covariance matrices using high frequency financial dataJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:30b9964e-fe95-4759-93e8-324de445fb33EnglishSymplectic ElementsFoundation for Open Access Statistics2022Baumann, SKlymak, MHigh frequency data typically exhibit asynchronous trading and microstructure noise, which can bias the covariances estimated by standard estimators. While a number of specialized estimators have been proposed, they have had limited availability in open source software. HighFrequencyCovariance is the first Julia package which implements specialized estimators for volatility, correlation and covariance using high frequency financial data. It also implements complementary algorithms for matrix regularization. This paper presents the issues associated with exploiting high frequency financial data and describes the volatility, covariance and regularization algorithms that have been implemented. We then demonstrate the use of the package using foreign exchange market tick data to estimate the covariance of the exchange rates between different currencies. We also perform a Monte Carlo experiment, which shows the accuracy gains that are possible over simpler covariance estimation techniques.
spellingShingle Baumann, S
Klymak, M
HighFrequencyCovariance: A Julia package for estimating covariance matrices using high frequency financial data
title HighFrequencyCovariance: A Julia package for estimating covariance matrices using high frequency financial data
title_full HighFrequencyCovariance: A Julia package for estimating covariance matrices using high frequency financial data
title_fullStr HighFrequencyCovariance: A Julia package for estimating covariance matrices using high frequency financial data
title_full_unstemmed HighFrequencyCovariance: A Julia package for estimating covariance matrices using high frequency financial data
title_short HighFrequencyCovariance: A Julia package for estimating covariance matrices using high frequency financial data
title_sort highfrequencycovariance a julia package for estimating covariance matrices using high frequency financial data
work_keys_str_mv AT baumanns highfrequencycovarianceajuliapackageforestimatingcovariancematricesusinghighfrequencyfinancialdata
AT klymakm highfrequencycovarianceajuliapackageforestimatingcovariancematricesusinghighfrequencyfinancialdata