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consider the following problem i am trying to find the monthly covariance matrix given daily data. i have the following codeimport datetime

import pandas as pd
import yfinance as yf
import numpy as np

tickers = ['AAPL', 'AMZN', 'XOM']
start_date1 = datetime.date(2010, 1, 2)
end_date1 = datetime.date(2019, 12, 31)
daily_data1 = yf.download(tickers, start=start_date1, end=end_date1)  # definere datasættet
daily_data1 = daily_data1['Adj Close'].dropna()


frames = [v for _, v in daily_data1.groupby(pd.Grouper(freq='M'))]

for month in frames:
    cov_matrix = (month.pct_change().apply(lambda x: np.log(1 + x)).cov())

so i think i would multiply the cov_matrix with the length of the given month by i am unsure if its that easy so multiply the cov_matrix*len(month)? if someone could confirm it. Thanks in advance

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    $\begingroup$ IMO the best way to find the monthly covariance matrix is to downsample the daily data to monthly and then directly compute the covariance on that monthly data. $\endgroup$
    – nbbo2
    Oct 29, 2022 at 10:52
  • $\begingroup$ how do I do it. not sure what you mean. will remind you that the above is a part of a more extensive code where I am using daily data I have just deleted all unnecessary for my question $\endgroup$
    – Robert
    Oct 29, 2022 at 11:10
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    $\begingroup$ @Robert you can downsample your daily data to monthly, using one line of Python code: daily_data1.resample('M').last() will give you the last day of each month for the corresponding daily_data1 dataframe. Now transform said monthly data to returns and compute the (rolling) covariance matrix on that data. $\endgroup$
    – Pleb
    Oct 30, 2022 at 21:09
  • $\begingroup$ The nice part about @nbbo2 suggestion is that alignment and weak auto correlation and cross correlation effects are less of a problem for monthly observations. The daily alignment (perhaps of last trade) is less important for returns spanning a month. The effects of correlation are included in the monthly returns. See also: quant.stackexchange.com/questions/71574/… $\endgroup$
    – krkeane
    Dec 1, 2022 at 18:10

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