Wei-Xing Zhou, Yun-Shi Dai, Kiet Tuan Duong, Peng-Fei Dai
arXiv 24 Oct 2023 · Finance — Statistical Finance · publishedJournal of Economic Behavior & Organization (2023) · 32 citations (OpenAlex)
arXiv:2310.16850 · PDF · DOI · OpenAlex · Extracted main text
The ongoing Russia-Ukraine conflict between two major agricultural powers has posed significant threats and challenges to the global food system and world food security. Focusing on the impact of the conflict on the global agricultural market, we propose a new analytical framework for tail dependence, and combine the Copula-CoVaR method with the ARMA-GARCH-skewed Student-t model to examine the tail dependence structure and extreme risk spillover between agricultural futures and spots over the pre- and post-outbreak periods. Our results indicate that the tail dependence structures in the futures-spot markets of soybean, maize, wheat, and rice have all reacted to the Russia-Ukraine conflict. Furthermore, the outbreak of the conflict has intensified risks of the four agricultural markets in varying degrees, with the wheat market being affected the most. Additionally, all the agricultural futures markets exhibit significant downside and upside risk spillovers to their corresponding spot markets before and after the outbreak of the conflict, whereas the strengths of these extreme risk spillover effects demonstrate significant asymmetries at the directional (downside versus upside) and temporal (pre-outbreak versus post-outbreak) levels.
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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 | Feng, F., Jia, N., Lin, F (2023) Quantifying the impact of Russia-Ukraine crisis on food security and trade pattern: evidence from a structural general equilibri… | 0.644 | 2 | 2 | 100% |
| 2 | 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% |
| 3 | Abadie, A (2002) Bootstrap tests for distributional treatment effects in instrumental variable models | 0.405 | 1 | 1 | 100% |
| 4 | Adekoya, O.B., Asl, M.G., Oliyide, J.A., Izadi, P (2023) Multifractality and cross-correlation between the crude oil and the European and non-European stock markets during the Russia-Uk… | 0.405 | 1 | 1 | 100% |
| 5 | Adekoya, O.B., Oliyide, J.A., Yaya, O.S., Al-Faryan, M.A.S (2022) Does oil connect differently with prominent assets during war? Analysis of intra-day data during the Russia-Ukraine saga | 0.405 | 1 | 1 | 100% |
| 6 | Adrian, T., Brunnermeier, M.K (2016) CoVaR | 0.405 | 1 | 1 | 100% |
| 7 | Ahmed, S., Hasan, M.M., Kamal, M.R (2022) Russia-Ukraine crisis: The effects on the European stock market | 0.405 | 1 | 1 | 100% |
| 8 | Aloui, R., Ben Aiessa, M.S., Nguyen, D.K (2011) Global financial crisis, extreme interdependences, and contagion effects: The role of economic structure? | 0.405 | 1 | 1 | 100% |
| 9 | Arndt, C., Diao, X., Dorosh, P., Pauw, K., Thurlow, J (2023) The Ukraine war and rising commodity prices: implications for developing countries | 0.405 | 1 | 1 | 100% |
| 10 | Arzandeh, M., Frank, J (2019) Price discovery in agricultural futures markets: Should we look beyond the best bid-ask spread? | 0.405 | 1 | 1 | 100% |
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