Yun-Shi Dai, Peng-Fei Dai, Wei-Xing Zhou
arXiv 2 Apr 2024 · Econometrics · 1 citations (OpenAlex)
arXiv:2404.01641 · PDF · DOI · OpenAlex · Extracted main text
The current international landscape is turbulent and unstable, with frequent outbreaks of geopolitical conflicts worldwide. Geopolitical risk has emerged as a significant threat to regional and global peace, stability, and economic prosperity, causing serious disruptions to the global food system and food security. Focusing on the international food market, this paper builds different dimensions of geopolitical risk measures based on the random matrix theory and constructs single- and two-factor GJR-GARCH-MIDAS models with fixed time span and rolling window, respectively, to investigate the impact of geopolitical risk on food market volatility. The findings indicate that modeling based on rolling window performs better in describing the overall volatility of the wheat, maize, soybean, and rice markets, and the two-factor models generally exhibit stronger explanatory power in most cases. In terms of short-term fluctuations, all four staple food markets demonstrate obvious volatility clustering and high volatility persistence, without significant asymmetry. Regarding long-term volatility, the realized volatility of wheat, maize, and soybean significantly exacerbates their long-run market volatility. Additionally, geopolitical risks of different dimensions show varying directions and degrees of effects in explaining the long-term market volatility of the four staple food commodities. This study contributes to the understanding of the macro-drivers of food market fluctuations, provides useful information for investment using agricultural futures, and offers valuable insights into maintaining the stable operation of food markets and safeguarding 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 | Caldara, D., Iacoviello, M (2022) Measuring Geopolitical Risk | 1.000 | 8 | 3 | 100% |
| 2 | Engle, R.F., Ghysels, E., Sohn, B (2013) Stock market volatility and macroeconomic fundamentals | 0.811 | 4 | 2 | 100% |
| 3 | Asgharian, H., Hou, A.J., Javed, F (2013) The importance of the macroeconomic variables in forecasting stock return variance: A GARCH-MIDAS approach | 0.644 | 2 | 2 | 100% |
| 4 | Conrad, C., Kleen, O (2020) Two are better than one: Volatility forecasting using multiplicative component GARCH-MIDAS models | 0.644 | 2 | 2 | 100% |
| 5 | Dai, P.F., Xiong, X., Zhou, W.X (2021) A global economic policy uncertainty index from principal component analysis self | 0.644 | 2 | 2 | 100% |
| 6 | Plerou, V., Gopikrishnan, P., Rosenow, B., Amaral, L., Guhr, T., Sta… (2002) Random matrix approach to cross correlations in financial data | 0.644 | 2 | 2 | 100% |
| 7 | Wei, Y., Liu, J., Lai, X., Hu, Y (2017) Which determinant is the most informative in forecasting crude oil market volatility: Fundamental, speculation, or uncertainty? | 0.644 | 2 | 2 | 100% |
| 8 | Abid, I., Dhaoui, A., Kaabia, O., Tarchella, S (2023) Geopolitical risk on energy, agriculture, livestock, precious and industrial metals: New from a Markov model | 0.511 | 2 | 1 | 100% |
| 9 | Gong, X., Xu, J (2022) Geopolitical risk and dynamic connectedness between commodity markets | 0.511 | 2 | 1 | 100% |
| 10 | Ahmed, S., Hasan, M.M., Kamal, M.R (2022) Russia-Ukraine crisis: The effects on the European stock market | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 66 scored citations.