跳至正文
返回文库全部文档

收益、波动率与交易量的结构模型

文章 arXiv papers · 作者: Andrea Bucci et al.
阅读原文

总结

本文提出结构矩阵自回归模型，在高维情形下联合研究资产收益、已实现波动率和交易量。该模型旨在捕捉变量间的动态溢出效应和资产间的依赖关系，同时比传统向量自回归模型采用更简约的参数化方式。研究根据混合分布假说和有效市场理论所提供的结构性约束识别模型。

实证分析使用道琼斯工业平均指数成分股 2021 年至 2025 年的日频数据。研究发现，波动率是交易活动的主要驱动因素。预测误差方差分解表明，短期内成交量动态主要受内部冲击影响，而在较长时间范围内，跨资产溢出效应解释了超过一半的成交量变化。围绕 FOMC 公告的事件研究发现，公告日活动中的信息成分上升，随后迅速均值回归。这些结果取决于所选股票、样本期、识别约束和模型设定。

核心观点

  • SMAR 框架联合建模跨资产收益、已实现波动率和成交量。
  • 其参数化旨在以简约方式捕捉溢出效应和横截面依赖关系。
  • 分析结果显示，波动率是交易活动的主要驱动因素。
  • 在较长时间范围内，跨资产溢出效应解释了更大比例的成交量变化。
  • FOMC 公告日的信息性活动增加，随后迅速均值回归。

标签

股票波动率市场微观结构统计学事件驱动
全文
# 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.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。