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Suppose we have 2 time series of market data, one for each security and we want to correlate between these 2 securities. My question is

  1. How do we handle gaps of missing data in the time series? Imagine the time series is one day tick data of a stock price and we have a 10 mins gap of missing data sometime during the day.

  2. How do we correlate the tick-by-tick market data of these 2 securities that do not happen at the same time for each tick? If we correlate them in the same time intervals, what price do we use?

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  • $\begingroup$ You could correlate only where data for both securities is available. Two securities rarely trade at the same nanosecond, so you'll have to create some discrete time interval (eg, 1 minute) for correlation purposes. You can use the median price for the minute to correlate. $\endgroup$
    – user59
    Commented Nov 8, 2014 at 16:17

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Looking for the same issue, I found an article by de Jong(1997). In section 2 you can find a method for estimation of covariances and correlations between irregularly spaced data. Also look at the article by Jonas Andersson where some interpolation methods and method form de Jong are presented and compared together. Hope it helps.

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  • $\begingroup$ The first link is dead but try this one instead. $\endgroup$
    – Luciano
    Commented Sep 20, 2017 at 7:25
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One assumption is that both (or more) instruments are liquid enough to offer a market (both sides).

You can

  1. use the bid/ask/mid (your choice) or

  2. "conflate" (implemented by the big boys on their data feeds). i.e. 1 second conflation: if no trade, send out last trade price (or assume so in your application).

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  • $\begingroup$ This is not very clear. You should give an example or elaborate more to make this answer really valuable. $\endgroup$
    – SRKX
    Commented Jun 11, 2016 at 6:37

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