Studies from Saitama University Further Understanding of Risk Management (Crisis-Regime Dynamic Volatility Spillovers in U.S. Commodity Markets: A Bayesian Mixture-Identified SVAR Approach): Insurance – Risk Management

2026 MAY 13 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- New study results on risk management have been published. According to news originating from Saitama, Japan, by NewsRx correspondents, research stated, “Conventional VAR-based volatility spillover measures rely on homoskedasticity and single-Gaussian assumptions, limiting their ability to capture structural breaks and heterogeneous shocks during crises.”

Funders for this research include Guangdong Basic And Applied Basic Research Foundation of China; Guangdong Medical University Doctoral Research Foundation.

The news editors obtained a quote from the research from Saitama University: “This study develops a flexible framework to analyze volatility transmission in U.S. commodity markets under multiple crisis regimes. We propose a Bayesian Structural Vector Autoregressive Mixture Normal (BSVAR-MIX) model that embeds finite normal mixtures within a mixture-based heteroskedastic structural VAR framework. The model combines generalized forecast error variance decomposition with posterior-probability weighting. Daily data for eight U.S. benchmark commodities across food, energy, and precious metals markets are examined over the 2008-2016 global financial crisis and the 2017-2025 multi-crisis period, including COVID-19 and the Russia-Ukraine conflict. The BSVAR-MIX framework provides a flexible descriptive setting for capturing multimodal shocks, heteroskedastic volatility states, and regime-dependent spillover patterns in commodity markets. Empirically, Gold and oil dominate systemic volatility transmission, soybeans amplify food-energy spillovers, while coal and wheat exhibit rising fragility under policy and geopolitical shocks.”

According to the news editors, the research concluded: “Assets commonly viewed as safe havens may contribute to systemic stress during extreme events. Overall, the framework offers a robust tool for structural shock identification and cross-commodity risk monitoring relevant to U.S. macroprudential policy.”

For more information on this research see: Crisis-Regime Dynamic Volatility Spillovers in U.S. Commodity Markets: A Bayesian Mixture-Identified SVAR Approach. Risks, 2026,14(4):75. (Risks - http://www.mdpi.com/journal/risks). The publisher for Risks is MDPI AG.

A free version of this journal article is available at https://doi.org/10.3390/risks14040075.

Our news journalists report that additional information may be obtained by contacting Xinyan Deng, Graduate School of Humanities and Social Sciences, Saitama University, 255 Shimo-Okubo, Sakura-ku, Saitama 338-8570, Japan. Additional authors for this research include Kentaka Aruga, Chaofeng Tang.

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