How to compute realized volatility I am provided with a dataset consisting of an open book and a trade book
The open book data has the following columns:

*

*Bid price/size 1

*Bid price/size 2

*Ask price/size 1

*Ask price/size 2

*time id

*seconds in bucket

The trade book data has the following columns:

*

*time id

*seconds in bucket

*price

*size

*order count

We have to estimate the realized volatility using this dataset. It is quite clear that for doing this, I have to construct new features using the data already given.
My question is 2 fold;
1. Which of the given columns is the most related with realized volatility?
2. How could I make new features using the given ones?

I would begin with

*

*Realized Volatility: A Review by Michael McAleer & Marcelo C. Medeiros

*Realised Kernels in Practice: Trades and Quotes by Barndorff-Nielsen et al

Both are available online.
