Yun-Shi Dai, Peng-Fei Dai, Stéphane Goutte, Duc Khuong Nguyen, Wei-Xing Zhou
arXiv 25 Jan 2025 · Econometrics · publishedRisk Analysis (2025) · 4 citations (OpenAlex)
arXiv:2501.15173 · PDF · DOI · OpenAlex · Extracted main text
Stable and efficient food markets are crucial for global food security, yet international staple food markets are increasingly exposed to complex risks, including intensified risk contagion and escalating external uncertainties. This paper systematically investigates risk spillovers in global staple food markets and explores the key determinants of these spillover effects, combining innovative decomposition-reconstruction techniques, risk connectedness analysis, and random forest models. The findings reveal that short-term components exhibit the highest volatility, with futures components generally more volatile than spot components. Further analysis identifies two main risk transmission patterns, namely cross-grain and cross-timescale transmission, and clarifies the distinct roles of each component in various net risk spillover networks. Additionally, price drivers, external uncertainties, and core supply-demand indicators significantly influence these spillover effects, with heterogeneous importance of varying factors in explaining different risk spillovers. This study provides valuable insights into the risk dynamics of staple food markets, offers evidence-based guidance for policymakers and market participants to enhance risk warning and mitigation efforts, and supports the stabilization of international food markets and the safeguarding of global food security.
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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 | Diebold, F.X., Yilmaz, K (2012) Better to give than to receive: Predictive directional measurement of volatility spillovers | 0.737 | 3 | 2 | 100% |
| 2 | Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q.,… (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis | 0.737 | 3 | 2 | 100% |
| 3 | Naeem, M.A., Chatziantoniou, I., Gabauer, D., Karim, S (2024) Measuring the G20 stock market return transmission mechanism: Evidence from the $R^2$ connectedness approach | 0.737 | 3 | 2 | 100% |
| 4 | Wei, D., Gephart, J.A., Iizumi, T., Ramankutty, N., Davis, K.F (2023) Key role of planted and harvested area fluctuations in US crop production shocks | 0.737 | 3 | 2 | 100% |
| 5 | Basak, S., Pavlova, A (2016) A model of financialization of commodities | 0.644 | 2 | 2 | 100% |
| 6 | Bianchi, R.J., Fan, J.H., Todorova, N (2020) Financialization and de-financialization of commodity futures: A quantile regression approach | 0.644 | 2 | 2 | 100% |
| 7 | Ji, Q., Bouri, E., Roubaud, D., Shahzad, S.J.H (2018) Risk spillover between energy and agricultural commodity markets: A dependence-switching CoVaR-copula model | 0.644 | 2 | 2 | 100% |
| 8 | Lin, A.J., Chang, H.Y., Hsiao, J.L (2019) Does the Baltic Dry Index drive volatility spillovers in the commodities, currency, or stock markets? | 0.644 | 2 | 2 | 100% |
| 9 | Tiwari, A.K., Abakah, E.J.A., Adewuyi, A.O., Lee, C.C (2022) Quantile risk spillovers between energy and agricultural commodity markets: Evidence from pre and during COVID-19 outbreak | 0.644 | 2 | 2 | 100% |
| 10 | Wright, B.D (2011) The economics of grain price volatility | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 63 scored citations.