Using Trading Volume to Predict Extreme Volatility

Summary

The document studies how trading volume and volatility interact, with particular attention to periods when both are elevated. It describes a conditional-distribution analysis: volatility distributions are examined across volume levels, modeled with a power law and exponential cutoff, and rescaled to collapse onto a common curve. It also defines local maximum volatility (LMV) as the largest volatility within a given volume range and finds that LMV rises with logarithmic volume.

The reported empirical results suggest volume can help estimate maximum volatility both within the same day and over a near-future period. A joint conditional probability using both volume and volatility performs better for forecasting the largest next-day volatility than either input alone. The summary provides no asset, sample, precise horizon, validation design, or forecast error measures, so it does not establish how well the relationship generalizes across markets or supports a deployable trading strategy.

Key ideas

• Volatility conditional on trading volume is described by a power law with an exponential cutoff.
• Rescaling conditional volatility distributions by volume brings them onto a common curve.
• Local maximum volatility measures the highest volatility observed within a volume range.
• Logarithmic volume is strongly associated with local maximum volatility.
• Combining volume and volatility improves next-day maximum-volatility prediction over using either alone.

Tags

# Predicting market instability: New dynamics between volume and volatility


# Predicting market instability: New dynamics between volume and volatility









Econophysics and econometrics agree that there is a correlation between volume and volatility in a time series. Using empirical data and their distributions, we further investigate this correlation and discover new ways that volatility and volume interact, particularly when the levels of both are high. We find that the distribution of the volume-conditional volatility is well fit by a power-law function with an exponential cutoff. We find that the volume-conditional volatility distribution scales with volume, and collapses these distributions to a single curve. We exploit the characteristics of the volume-volatility scatter plot to find a strong correlation between logarithmic volume and a quantity we define as local maximum volatility (LMV), which indicates the largest volatility observed in a given range of trading volumes. This finding supports our empirical analysis showing that volume is an excellent predictor of the maximum value of volatility for both same-day and near-future time periods. We also use a joint conditional probability that includes both volatility and volume to demonstrate that invoking both allows us to better predict the largest next-day volatility than invoking either one alone.

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.