Andrea Bucci, Giulio Palomba, Eduardo Rossi
arXiv 6 Jun 2026 · Econometrics
arXiv:2606.08141 · PDF · DOI · OpenAlex · Extracted main text
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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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Chen, Rong and Xiao, Han and Yang, Dan (2021) Autoregressive models for matrix-valued time series | 1.000 | 10 | 4 | 100% |
| 2 | Kilian, L. and Lüktepohl, H (2017) Structural Vector Autoregressive Analysis | 1.000 | 6 | 3 | 100% |
| 3 | Tauchen, George E. and Pitts, Mark (1983) The Price Variability-Volume Relationship on Speculative Markets | 0.928 | 4 | 3 | 100% |
| 4 | Andersen, Torben G (1996) Return Volatility and Trading Volume: An Information Flow Interpretation of Stochastic Volatility | 0.644 | 2 | 2 | 100% |
| 5 | Bessembinder, Hendrik and Seguin, Paul J (1993) Price Volatility, Trading Volume, and Market Depth: Evidence from Futures Markets | 0.644 | 2 | 2 | 100% |
| 6 | Black, F (1976) Studies of Stock Price Volatility Changes | 0.644 | 2 | 2 | 100% |
| 7 | Fama, Eugene F (1970) Efficient Capital Markets: A Review of Theory and Empirical Work | 0.644 | 2 | 2 | 100% |
| 8 | Gallant, A. Ronald and Rossi, Peter E. and Tauchen, George (1992) Stock Prices and Volume | 0.644 | 2 | 2 | 100% |
| 9 | Lucca, David O. and Moench, Emanuel (2015) The Pre-FOMC Announcement Drift | 0.644 | 2 | 2 | 100% |
| 10 | Bollerslev, Tim and Li, Jia and Xue, Yuan (2018) Volume, Volatility, and Public News Announcements | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 41 scored citations.