Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
169 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Uncovering the Sino-US dynamic risk spillovers effects: Evidence from agricultural futures markets (2403.01745v1)

Published 4 Mar 2024 in econ.GN, q-fin.EC, and q-fin.RM

Abstract: Agricultural products play a critical role in human development. With economic globalization and the financialization of agricultural products continuing to advance, the interconnections between different agricultural futures have become closer. We utilize a TVP-VAR-DY model combined with the quantile method to measure the risk spillover between 11 agricultural futures on the futures exchanges of US and China from July 9,2014, to December 31,2022. This study yielded several significant findings. Firstly, CBOT corn, soybean, and wheat were identified as the primary risk transmitters, with DCE corn and soybean as the main risk receivers. Secondly, sudden events or increased economic uncertainty can increase the overall risk spillovers. Thirdly, there is an aggregation of risk spillovers amongst agricultural futures based on the dynamic directional spillover results. Lastly, the central agricultural futures under the conditional mean are CBOT corn and soybean, while CZCE hard wheat and long-grained rice are the two risk spillover centers in extreme cases, as per the results of the spillover network and minimum spanning tree. Based on these results, decision-makers are advised to safeguard against the price risk of agricultural futures under sudden economic events, and investors can utilize the results to construct a superior investment portfolio by taking different agricultural product futures as risk-leading indicators according to various situations.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (40)
  1. Modelling time and frequency connectedness among energy, agricultural raw materials and food markets. Journal of Applied Economics 25, 644–662. doi:10.1080/15140326.2022.2056300.
  2. CoVaR. American Economic Review 106, 1705–1741. doi:10.1257/aer.20120555.
  3. How index investment impacts commodities: A story about the financialization of agricultural commodities. Economic Modelling 80, 23–33. doi:10.1016/j.econmod.2018.04.007.
  4. Refined measures of dynamic connectedness based on time-varying parameter vector autoregressions. Journal of Risk and Financial Management 13, 84. doi:10.3390/jrfm13040084.
  5. On bank return and volatility spillovers: Identifying transmitters and receivers during crisis periods. International Review of Economics & Finance 82, 156–176. doi:10.1016/j.iref.2022.06.009.
  6. Volatility spillovers in commodity markets: A large t-vector autoregressive approach. Energy Economics 85, 104555. doi:10.1016/j.eneco.2019.104555.
  7. A model of financialization of commodities. Journal of Finance 71, 1511–1556. doi:10.1111/jofi.12408.
  8. Financialization and de-financialization of commodity futures: A quantile regression approach. International Review of Financial Analysis 68, 101451. doi:10.1016/j.irfa.2019.101451.
  9. Extreme spillovers across asian-pacific currencies: A quantile-based analysis. International Review of Financial Analysis 72, 101605. doi:10.1016/j.irfa.2020.101605.
  10. The supply of storage. American Economic Review 48, 50–72.
  11. Extreme spillovers among fossil energy, clean energy, and metals markets: Evidence from a quantile-based analysis. Energy Economics 107, 105880. doi:10.1016/j.eneco.2022.105880.
  12. Extreme risk spillover of the oil, exchange rate to Chinese stock market: Evidence from implied volatility indexes. Energy Economics 107, 105857. doi:10.1016/j.eneco.2022.105857.
  13. Asymmetric volatility spillover among global oil, gold, and Chinese sectors in the presence of major emergencies. Resources Policy 82, 103579. doi:10.1016/j.resourpol.2023.103579.
  14. Returns to speculators: Telser versus keynes. Journal of Political Economy 68, 396–404. doi:10.1086/258347.
  15. Measuring financial asset return and volatility spillovers, with application to global equity markets. Economic Journal 119, 158–171. doi:10.1111/j.1468-0297.2008.02208.x.
  16. Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting 28, 57–66. doi:10.1016/j.ijforecast.2011.02.006.
  17. On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics 182, 119–134. doi:10.1016/j.jeconom.2014.04.012.
  18. Extreme risk spillovers between crude oil and stock markets. Energy Economics 51, 455–465. doi:10.1016/j.eneco.2015.08.007.
  19. Dynamic spillover between traditional energy markets and emerging green markets: Implications for sustainable development. Resources Policy 82, 103483. doi:10.1016/j.resourpol.2023.103483.
  20. Market interdependence and volatility transmission among major crops. Agricultural Economics 47, 141–155. doi:10.1111/agec.12184.
  21. How far do shocks move across borders? Examining volatility transmission in major agricultural futures markets. European Review of Agricultural Economics 41, 301–325. doi:10.1093/erae/jbt020.
  22. Oil prices and agricultural commodity markets: Evidence from pre and during COVID-19 outbreak. Resources Policy 73, 102236. doi:10.1016/j.resourpol.2021.102236.
  23. Modelling extreme risk spillovers in the commodity markets around crisis periods including COVID19. Annals of Operations Research doi:10.1007/s10479-022-04522-9.
  24. Risk spillover between energy and agricultural commodity markets: A dependence-switching CoVaR-copula model. Energy Economics 75, 14–27. doi:10.1016/j.eneco.2018.08.015.
  25. Correlation between agricultural markets in dynamic perspective-evidence from China and the US futures markets. Physica A 464, 83–92. doi:10.1016/j.physa.2016.07.048.
  26. Spillovers and directional predictability with a cross-quantilogram analysis: The case of US and Chinese agricultural futures. Journal of Futures Markets 36, 1231–1255. doi:10.1002/fut.21779.
  27. Dynamic spillover effects among crude oil, precious metal, and agricultural commodity futures markets. Energy Economics 62, 19–32. doi:10.1016/j.eneco.2016.12.011.
  28. Risk transmission between Chinese and US agricultural commodity futures markets - a CoVaR approach. Sustainability 11, 239. doi:10.3390/su11010239.
  29. Volatility and price jumps in agricultural future prices - Evidence from wheat options. American Journal of Agricultural Economics 86, 1018–1031. doi:10.1111/j.0002-9092.2004.00650.x.
  30. Regression quantiles. Econometrica 46, 33–50. doi:10.2307/1913643.
  31. Correlations between biofuels and related commodities before and during the food crisis: A taxonomy perspective. Energy Economics 34, 1380–1391. doi:10.1016/j.eneco.2012.06.016.
  32. Dynamic price discovery in Chinese agricultural futures markets. Journal of Asian Economics 76, 101370. doi:10.1016/j.asieco.2021.101370.
  33. High-frequency volatility connectedness between the US crude oil market and China’s agricultural commodity markets. Energy Economics 76, 424–438. doi:10.1016/j.eneco.2018.10.031.
  34. Market efficiency in agricultural futures markets. Applied Economics 34, 1519–1532. doi:10.1080/00036840110102761.
  35. Dynamic spillovers among major energy and cereal commodity prices. Energy Economics 43, 225–243. doi:10.1016/j.eneco.2014.03.004.
  36. Measuring extreme risk spillovers across international stock markets: A quantile variance decomposition analysis. North American Journal of Economics and Finance 51, 101098. doi:10.1016/j.najef.2019.101098.
  37. Connectedness and directional spillovers in energy sectors: International evidence. Applied Economics 54, 2554–2569. doi:10.1080/00036846.2021.1998326.
  38. Structure dependence between oil and agricultural commodities returns: The role of geopolitical risks. Energy 219, 119584. doi:10.1016/j.energy.2020.119584.
  39. Dynamic volatility spillover effects between oil and agricultural products. International Review of Financial Analysis 69, 101465. doi:10.1016/j.irfa.2020.101465.
  40. What Bayesian quantiles can tell about volatility transmission between the major agricultural futures? Agricultural Economics-Zemedelska Ekonomika 66, 215–225. doi:10.17221/127/2019-AGRICECON.
Citations (1)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now