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

2026-06-06

AI Summary

This study investigates the dynamic interactions and structural drivers among asset returns, realized volatility, and trading volume in high-dimensional financial data. To this end, the authors propose a Structured Matrix Autoregressive (SMAR) model that integrates identification constraints derived from the mixture-of-distributions hypothesis and the efficient market hypothesis, thereby preserving parameter parsimony while effectively capturing dynamic spillovers and cross-sectional dependencies. Empirical results reveal that volatility is a primary driver of trading volume; short-term volume dynamics are dominated by idiosyncratic shocks, whereas over 50% of long-term variation stems from cross-asset spillovers. Moreover, information-driven trading significantly intensifies on FOMC announcement days and exhibits rapid mean reversion. The proposed framework offers an identifiable and interpretable approach to modeling dynamics in high-dimensional financial systems.

Abstract

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.

Problem

• joint dynamics
• volatility
• trading volume
• asset returns
• spillovers

Innovation

• Structural Matrix Autoregressive
• Dynamic Spillovers
• Parsimonious Parameterization
• Mixture of Distributions Hypothesis
• Cross-sectional Dependence