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My aim is to estimate the covariance matrix using 10 Forex high-frequency data. I found the Julia package, HighFrequencyCovariance, HighFrequencyCovariance.jl - Algorithms for efficiently estimating covariance matrices with high frequency financial data, showing the different covariance estimators I might use. However, I am unable to understand how to put my dataframe so as I can proceed further in my analysis.

For example, I don’t understand the following codes:

using HighFrequencyCovariance

using DataFrames

df = DataFrame(:stock => [:A,:B,:A,:A,:A,:B,:A,:B,:B], :time => [1,2,3,4,5,5,6,7,8],

:logprice => [1.01,2.0,1.011,1.02,1.011,2.2,1.0001,2.2,2.3])

ts = SortedDataFrame(df, :time, :stock, :logprice)

I will be using 1-min return. The data structure of say, EUR/AUD has the headers, local time, Open, High, Low, Close prices and Volume.

I know how to import each dataset in Julia (have 10 datasets since I will be using 10 Forex). However, I don’t understand how to proceed and what will the time and log prices represent in this context.

Also, are there better covariance estimators (where the codes are already available) I can use, to work with the HF data?

Can anyone please enlighten me on this issue?