Nektarios Aslanidis, Aurelio Bariviera, George Kapetanios, Vasilis Sarafidis, Alexia Ventouri
arXiv 30 Sep 2026 · Econometrics
arXiv:2610.00581 · PDF · Extracted main text
We study the anatomy of commodity risk by distinguishing micro, market-level, and economy-wide sources. We develop a two-stage "divide-and-conquer" framework that allows sensitivities to these risk sources to vary across commodities while treating economy-wide risk as latent. The first stage uses defactored instrumental-variable estimation to recover commodity-specific sensitivities to micro and market conditions. The second combines principal components with high-dimensional variable selection to identify an observable representation of macro-financial risk. We then construct Risk Intensity Indices (RIIs), which combine estimated sensitivities with prevailing risk conditions to quantify the relative importance of each risk source on a common scale. Market risk is the largest component on average, accounting for about two fifths of total risk intensity and more than half for energy commodities. Risk intensity is also highly concentrated across individual commodities: the top 20% account for approximately half of micro and market risk intensity, whereas macro risk is more broadly dispersed. The composition of risk varies substantially across sectors and over time, with market risk becoming particularly prominent during episodes of commodity-market stress. Micro and market RIIs also contain information about future volatility and absolute returns. These findings provide investors, risk managers, and policymakers with a diagnostic of where commodity risk is concentrated, which risk layers are most important, and how their importance changes over time. More broadly, our divide-and-conquer framework provides a flexible approach to decomposing layered risk in settings where common risk is latent.
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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 | Milda Norkute, Vasilis Sarafidis, Takashi Yamagata, and Guowei Cui (2021) Instrumental variable estimation of dynamic linear panel data models with defactored regressors and a multifactor error structure self | 0.843 | 4 | 3 | 75% |
| 2 | Ke Tang and Wei Xiong (2012) Index investment and the financialization of commodities | 0.811 | 4 | 2 | 100% |
| 3 | George Kapetanios, Vasilis Sarafidis, and Alexia Ventouri (2026) Model selection in high-dimensional linear regression using boosting with multiple testing, 2026 self | 0.794 | 10 | 5 | 50% |
| 4 | Jushan Bai (2009) Panel Data Models with Interactive Fixed Effects | 0.737 | 4 | 3 | 50% |
| 5 | Seung C. Ahn and Alex R. Horenstein (2013) Eigenvalue ratio test for the number of factors | 0.737 | 3 | 3 | 67% |
| 6 | Jushan Bai and Serena Ng (2002) Determining the number of factors in approximate factor models | 0.737 | 3 | 3 | 67% |
| 7 | Dario Caldara and Matteo Iacoviello (2022) Measuring Geopolitical Risk | 0.737 | 3 | 3 | 67% |
| 8 | Suleyman Basak and Anna Pavlova A model of financialization of commodities | 0.737 | 3 | 2 | 100% |
| 9 | Gary Gorton and K. Geert Rouwenhorst (2006) Facts and fantasies about commodity futures | 0.644 | 2 | 2 | 100% |
| 10 | Steven D. Baker (2019) The financialization of storable commodities | 0.585 | 3 | 1 | 100% |
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