There is a large literature covering volatility forecasts with high-frequency tick data. Much of this has surrounded the concept of "realized volatility", such as:

Other efforts have looked at high/low data to improve the forecast without including all the tick data.

Robert Almgrem has a nice lecture on the subject as part of his "Time Series Analysis and Statistical Arbitrage" course at NYU.

What's the best way for forecast volatility using high-frequency data?

Note: A similar question was previously asked on Wilmott.

  • $\begingroup$ Both PDFs link to the same place (and it's a temp.pdf file, so maybe UPenn just copies whatever file you choose to temp.pdf temporarily?). Off-the-cuff thought: model volatility per-trade, not per-time-unit and then model trades-per-time-unit separately. $\endgroup$
    – user59
    Mar 18, 2011 at 15:24
  • $\begingroup$ The link to the second paper should now be correct. $\endgroup$ Sep 9, 2011 at 13:24
  • 1
    $\begingroup$ The links to Almgrem's lecture notes and class at NYU are very nice. Some good reading in there. $\endgroup$ Sep 9, 2011 at 13:26

3 Answers 3


Personally, I've dealt with volatility estimation using wavelets with HF data. Estimations seem reasonable and also it's fairly quick computation wise compared to other methods. There's quite a bit of literature on the subject, I would recommended starting off with An Introduction to Wavelets and Other Filtering Methods in Finance and Economics


Relevant paper:

Efficient Estimation of Volatility using High Frequency Data (Zumbach, Corsi, and Trapletti 2002)




PPS = Packets Per Second (wiki article: network packets)

The standard deviation of packets per second received from a liquidity source are directly related to the number of quotes per second, or the number of trades per second occurring on that liquidity source. Thus, the higher the number of network / data packets per second, the more volatility there will be on that specific venue, or the market as a whole. std(PPS) can be used as a leading indicator of volatility in a sub-second trading environment.

This answer is based on my personal experience of analyzing multiple liquidity providers (data sources) in real-time to predict / mitigate volatility or stressed markets.

  • $\begingroup$ This would correlate with volume, not volatility $\endgroup$ Oct 9, 2014 at 20:59

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