A Structural Model of Returns, Volatility, and Trading Volume

Summary

The paper proposes a Structural Matrix Autoregressive model to study asset returns, realized volatility, and trading volume together in a large-dimensional setting. It is designed to capture both dynamic spillovers among the variables and dependence across assets, while keeping the parameterization more parsimonious than a conventional vector autoregression. Structural restrictions informed by the Mixture of Distributions Hypothesis and efficient market theory are used to identify the model.

The empirical analysis uses daily data for Dow Jones Industrial Average constituents from 2021 to 2025. Its findings indicate that volatility is the main driver of trading activity. Forecast error variance decompositions suggest that internal shocks dominate volume dynamics at short horizons, while cross-asset spillovers explain more than half of volume variation at longer horizons. An event study around FOMC announcements finds a rise in the informative component of activity on announcement days, followed by rapid mean reversion. These results are tied to the selected equities, sample period, identification restrictions, and model specification.

Key ideas

• The SMAR framework jointly models returns, realized volatility, and volume across assets.
• Its parameterization aims to capture spillovers and cross-sectional dependence parsimoniously.
• The reported analysis finds volatility to be the primary driver of trading activity.
• Cross-asset spillovers account for a larger share of volume variation at longer horizons.
• FOMC announcement days show increased informative activity followed by rapid mean reversion.

Tags

# A Structural Matrix Autoregressive Model for the Joint Dynamics of Volume, Volatility, and Returns


# A Structural Matrix Autoregressive Model for the Joint Dynamics of Volume, Volatility, and Returns









This paper proposes a Structural Matrix Autoregressive (SMAR) model for the joint analysis of asset returns, realized volatility, and trading volume in a large-dimensional setting. This framework simultaneously captures dynamic spillovers across financial variables and cross-sectional dependence across assets while preserving a parsimonious parameterization relative to conventional vector autoregressive models. The model is estimated on daily data for the constituents of the Dow Jones Industrial Average over the period 2021-2025 and is structurally identified through restrictions consistent with the Mixture of Distributions Hypothesis and efficient market theory. The empirical findings indicate that volatility is the primary driver of trading activity, suggesting that informational shocks are predominantly incorporated into markets through price variability. Forecast error variance decompositions further reveal that, although internal shocks dominate short-term volume dynamics, cross-asset spillovers account for more than 50% of trading volume variation at longer horizons. Finally, an event-study analysis around FOMC announcements supports the proposed decomposition by identifying significant increases in the informative component of trading activity on announcement days followed by rapid mean reversion.

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