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.
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