Estimating Instantaneous Volatility from Trading Volume and Order-Book Data

Summary

The document describes an empirical market invariant that links an instrument’s volatility with traded volume, average spread, and volume available in the order book. It uses this relationship to estimate what the authors call instantaneous volatility, positioning the measure as a short-horizon input for algorithmic trading and volatility decisions.

The authors report testing the relationship across multiple markets and asset classes, with no significant violations observed in their tests. They also say quantitative comparisons found the resulting estimate more closely reproduced realized volatility than a one-day-ahead GARCH(1,1) forecast. The summary does not specify the invariant’s formula, sampling frequency, test period, error metric, or market coverage, so it is not possible to judge how broadly the reported comparison generalizes. Its described inputs connect the estimate to market activity and order-book conditions, making the approach relevant to market microstructure as well as volatility measurement.

Key ideas

• The proposed invariant relates volatility to traded volume, average spread, and order-book volume.
• The invariant is used to construct a short-term instantaneous volatility estimate.
• The authors report testing the relationship across markets and asset classes without significant violations.
• Their comparison found the estimate reproduced realized volatility better than a one-day-ahead GARCH(1,1) forecast.
• The document omits the formula and evaluation details needed to assess generalizability.

Tags

# An instantaneous market volatility estimation


# An instantaneous market volatility estimation









Working on different aspects of algorithmic trading we empirically discovered a new market invariant. It links together the volatility of the instrument with its traded volume, the average spread and the volume in the order book. The invariant has been tested on different markets and different asset classes. In all cases we did not find significant violation of the invariant. The formula for the invariant was used for the volatility estimation, which we called the instantaneous volatility. Quantitative comparison showed that it reproduces realised volatility better than one-day-ahead GARCH(1,1) prediction. Because of the short-term prediction nature, the instantaneous volatility could be used by algo developers, volatility traders and other market professionals.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.