I'm interested in quantifying the impact of short term price volatility on a particular strategy I'm running. So far I'm simply calculating the standard deviation of log returns, but I'm a bit unsure on where to take it from here. Additionally - any textbooks/further reading on this sort of thing appreciated.

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    $\begingroup$ What is the frequency of your data? It sounds like you're looking for realized volatility estimators. In general, if you have a sub-minute frequency it is often a good idea to consider noise-robust estimators, since price-data (TAQ data) tend to be noisy (for equities). Here, liquidity of your considered instruments, also have an impact on your estimates and sampling scheme. $\endgroup$
    – Pleb
    Oct 7 at 11:15
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    $\begingroup$ I am using sub-minute frequency, thanks, I'll look into these key words. $\endgroup$ Oct 7 at 17:27
  • $\begingroup$ For noise-robust realized estimators you can take a look at the Realized kernel estimator of Barndorff-Nielsen et al. (2009) or the Preaveraging estimator of Jacod et al. (2009). They both exist in a multivariate version and are implemented in the (very detailed and thorough) R package called highfrequency. They recently updated their documentation of the package, which is available here. $\endgroup$
    – Pleb
    Oct 7 at 18:04
  • $\begingroup$ Reading their documentation will give you loads of references and applicable estimators besides the ones I've talked about above. I hope this helps :-) $\endgroup$
    – Pleb
    Oct 7 at 18:06
  • $\begingroup$ Thanks! This looks great. Much appreciated $\endgroup$ Oct 8 at 12:04

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