We develop a dynamic factor stochastic volatility-in-mean (SVM) specification for vector autoregressions (VARs) that embeds an SVM component within a dynamic factor stochastic volatility structure. A small number of latent volatility factors capture common movements in conditional variances, while volatility enters the conditional mean of the VAR. This specification allows time-varying uncertainty to influence macroeconomic dynamics through both second moments and expected outcomes while preserving tractability in large panels. We construct an efficient Markov chain Monte Carlo algorithm for estimation in this high-dimensional, non-Gaussian setting. Using quarterly data on twenty variables from the FRED-QD database, we compare predictive performance with the benchmark stochastic volatility VAR model. The dynamic factor SVM specification delivers superior forecasts for more variables during major macroeconomic disruptions such as the 2008 global financial crisis. The results indicate that allowing volatility to enter the mean captures an important transmission channel in macroeconomic dynamics.

翻译：本文提出了一种动态因子随机波动率均值向量自回归模型，该模型将随机波动率均值成分嵌入至动态因子随机波动率结构之中。通过少量潜在波动率因子捕捉条件方差的共同变动，同时允许波动率进入向量自回归的条件均值项。该设定在保持大面板数据可处理性的前提下，使时变不确定性通过二阶矩和预期结果两个渠道影响宏观经济动态。针对这种高维非高斯设定，我们构建了高效的马尔可夫链蒙特卡洛估计算法。基于FRED-QD数据库中二十个变量的季度数据，我们与基准随机波动率向量自回归模型进行了预测表现比较。动态因子随机波动率均值模型在2008年全球金融危机等重大宏观经济冲击期间，对更多变量产生了更优的预测结果。研究表明，允许波动率进入均值项能够捕捉宏观经济动态中的一个重要传导渠道。