I have price volume data of five stocks, sampled at 1 minute interval for six months. The data is quite noisy, lots of missing data and also some weired spikes. Can someone suggest me how to clean this dataset? The main objective is to estimate the volatility for the next month following the end of samples, what is the best method for this? Also how do I verify whether the volatility is dependent on market liquidity, based on this data. Can someone point me to easy tutorials/books that allows this kinds of data analysis. I have a good background on statistical data analysis but I am a complete noob to financial data. Any help will be much appreciated


1 Answer 1


If you are familiar with programming (which is required to deal with HF data), I would strongly recommend you to use the "HighFrequency" R package. It includes a lot of procedures to clean HF data and to estimate volatility.

You can find here a very good tutorial about the package.

If needed you can find here some good tutorials for R.

Credits: The package has been developed by Kris Boudt , Jonathan Cornelissen , Scott Payseur,Giang Nguyen and Maarten Schermer .


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