Main Article Content
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.