Daichi Hiraki, Siddhartha Chib, Yasuhiro Omori
arXiv 6 Apr 2026 · Statistics — Methodology
arXiv:2604.04529 · PDF · DOI · OpenAlex · Extracted main text
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.
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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 | Jamie L. Cross and Chenghan Hou and Gary Koop and Aubrey Poon (2023) Large stochastic volatility in mean VARs | 1.000 | 7 | 4 | 100% |
| 2 | Hiraki, Daichi and Chib, Siddhartha and Omori, Yasuhiro (2025) Stochastic volatility in mean: Efficient analysis by a generalized mixture sampler self | 0.843 | 4 | 4 | 75% |
| 3 | Kim, Sangjoon and Shephard, Neil and Chib, Siddhartha (1998) Stochastic volatility: likelihood inference and comparison with ARCH models self | 0.737 | 3 | 3 | 67% |
| 4 | Carriero, Andrea and Clark, Todd E and Marcellino, Massimiliano (2016) Common drifting volatility in large Bayesian VARs | 0.644 | 2 | 2 | 100% |
| 5 | Carriero, Andrea and Clark, Todd E and Marcellino, Massimiliano (2019) Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors | 0.644 | 2 | 2 | 100% |
| 6 | Davidson, Sharada Nia and Hou, Chenghan and Koop, Gary (2025) Investigating economic uncertainty using stochastic volatility in mean VARs: the importance of model size, order-invariance and… | 0.644 | 2 | 2 | 100% |
| 7 | de Jong, Piet and Shephard, Neil (1995) The simulation smoother for time series models | 0.511 | 4 | 2 | 25% |
| 8 | Durbin, James and Koopman, Siem Jan (2002) A simple and efficient simulation smoother for state space time series analysis | 0.511 | 2 | 2 | 50% |
| 9 | Cross, Jamie L and Hou, Chenghan and Poon, Aubrey (2020) Macroeconomic forecasting with large Bayesian VARs: Global-local priors and the illusion of sparsity | 0.511 | 2 | 1 | 100% |
| 10 | Aguilar, Omar and West, Mike (2000) Bayesian dynamic factor models and portfolio allocation | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 26 scored citations.