Using the Epps effect to detect discrete data generating processes: Dataset

Data consists of Trade and Quote data for 10 equities from the Johannesburg Stock Exchange. The data consists of 40 days of trading data starting from 2019-05-02 to 2019-06-28. The data has been processed to contain only the Automated Trade (AT) types. Furthermore, trades with the same time-stamp have been aggregated using a volume weighted average so that there is only one trade per time-stamp. Missing data is indicated with NaN's. <br><br>The 10 equities included are: FirstRand Limited (FSR), Shoprite Holdings Ltd (SHP), Absa Group Ltd (ABG), Nedbank Group Ltd (NED), Standard Bank Group Ltd (SBK), Sasol Ltd (SOL), Mondi Plc (MNP), Anglo American Plc (AGL), Naspers Ltd (NPN) and British American Tobacco Plc (BTI).<br><br>The data structure in each csv file is 11 columns, with the first being the times at which the assets have traded. The remaining 10 columns are the trade information of the tickers. The values in these columns are the trading price (aggregated). NaN means the asset did not trade at that time stamp.<br>The data should only be used to aid the reproducibility for the paper: Using the Epps effect to detect discrete data generating processes. The steps to reproduce our results can be found in our GitHub site: https://github.com/CHNPAT005/PCEPTG-EC. <br>The research focuses on empirically determining if correlations are an emerging property in high frequency finance. <br>The work is funded by the South African Statistical Association. The original data was sourced from Bloomberg Pro. The code for the research is done using Julia Pro.<br>DOI for the code: 10.25375/uct.12315167