Return Volatility and Trading Volume: An Information Flow Interpretation of Stochastic Volatility
从微观结构出发，构建了一个收益波动率与交易量的实证模型，将信息流建模为随机波动过程，改进了混合分布假说，并发现该模型优于标准版本，有助于理解波动率聚集的经济因素。
ABSTRACT The paper develops an empirical return volatility‐trading volume model from a microstructure framework in which informational asymmetries and liquidity needs motivate trade in response to information arrivals. The resulting system modifies the so‐called “Mixture of Distribution Hypothesis” (MDH). The dynamic features are governed by the information flow, modeled as a stochastic volatility process, and generalize standard ARCH specifications. Specification tests support the modified MDH representation and show that it vastly outperforms the standard MDH. The findings suggest that the model may be useful for analysis of the economic factors behind the observed volatility clustering in returns.