This article gives a nice outline of how daily data can be used to estimate cointegration on a monthly horizon.


I'd like to use the same method to use hourly data to estimate mean reversion on a horizon of a few days.

The scaling seems to be the same one month=approx 22 working days and, one day =24 hours.

I spoke to the author and he seemed to think that using hourly data to estimate the daily dynamics would not work but didn't explain why.

Q1.) I respect the author so I assume he's correct, but I'd still like to understand why you can't use the hourly data to estimate daily dynamics?

Q2.) Assuming he is is correct is there any way I can estimate cointegrating relationships that occurs over the period of days from higher frequency data?


  • 1
    $\begingroup$ This is not to confirm, that it's not possible, but to note it's a bit different. While daily data is uniform within a month, hourly data within 24hr is certainly not: there are trading hours, and the rest, which you'll somehow have to treat differently. $\endgroup$ – LazyCat Jan 25 '16 at 18:51
  • $\begingroup$ I take your point but if I just convert everything into hourly bars for fitting and prediction would this be OK? You still have jumps between days etc but using hourly bucketing like this is used routinely in trading (although not necessarily for cointegration analyses). $\endgroup$ – Bazman Jan 25 '16 at 19:29
  • $\begingroup$ Depends. What if one instrument trades after-hours and the other one doesn't. Or both don't trade much, and the spread widens a lot, etc. In my experience, it's not a good idea to mix hourly/minute date during trading hours and after. But as said, there should be a way to do it right. $\endgroup$ – LazyCat Jan 25 '16 at 23:10
  • $\begingroup$ Both instruments have the same trading hours, but liquidity can be an issue. I take your point that there must be correct method(s) to do it, hopefully someone will post some suggestions up. $\endgroup$ – Bazman Jan 26 '16 at 10:22

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