# Tag Info

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The average would be called the mid-price, not the best in my opinion, but that depends on your modeling. Another strategy is to weight the bid and offer prices according to size, also called the micro-price or bid-offer weighted price. This has the advantage of moving your calculated price closer to where it is traded as volume is depleted from whatever ...

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In the set of an index where all insturments are traded in the same time zone I would agree that vola pa from say weekly returns is lower than from daily returns. Besides this, the distribution of weekly returns should look "more" Gaussian than the one of daily returns. This is called aggregational Gaussianity e.g. in the paper by Rogers and Zhang. The term ...

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It is most common to use the "square root of time" method to scale volatility (i.e. standard deviation of returns) to a year (annualize it) if needed, i.e. if the estimate is based on a sample with higher frequency (daily, weekly,..). Mathematically this requires the underlying stochastic process $(X_t)_{t\in T}$ (I've omitted some technical prerequisites ...

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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 ...

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It is not entirely clear what you're after, since Method 1 from the question is a statistical model, while Method 2 is a statistical test. From the initial question, I'm going to make the assumption that what you're actually after is some number that summarises "momentum" on a given day. If this is the case, I would weakly prefer the Ljung-Box test ...

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