Home Price Connectedness in the Futures Markets of Livestock Commodities
Article
Licensed
Unlicensed Requires Authentication

Price Connectedness in the Futures Markets of Livestock Commodities

  • Dimitrios Panagiotou EMAIL logo
Published/Copyright: February 7, 2025

Abstract

The present study investigates the linkages among the futures prices of feeder cattle, live cattle and lean hogs in the US. This has been pursued using a flexible methodology that allows modelling price relationships at different parts of their joint distribution. Data are daily closing prices for the period between 1/1/2015 and 12/31/2023. According to the empirical results: i) livestock commodities boom together and crash (with one exception) together, ii) extreme price decreases are transmitted with higher intensity compared to extreme price increases, iii) transmission asymmetries in prices, between livestock commodities, can occur at the tails as well as at the median of the joint distributions. Lastly, opportunities for speculators to profit from the spread between the commodities of feeder cattle and live cattle can be present.

JEL Classification: Q14; C58; D47

Corresponding author: Dimitrios Panagiotou, Department of Economics, University of Ioannina, Ioannina, Greece, E-mail:

References

Adjemian, M. K., V. G. Bruno, and M. A. Robe. 2020. “Incorporating Uncertainty into Usda Commodity Price Forecasts.” American Journal of Agricultural Economics 102 (2): 696–712. https://doi.org/10.1002/ajae.12075.Search in Google Scholar

Anderson, J. D., A. M. McKenzie, and J. L. Mitchell. 2021. “Price Determination and Price Discovery in the Fed Cattle Market: A Review of Economic Concepts and Empirical Work.” Economics 34 (4): 652.Search in Google Scholar

Ando, T., M. Greenwood-Nimmo, and Y. Shin. 2022. “Quantile Connectedness: Modeling Tail Behavior in the Topology of Financial Networks.” Management Science 68 (4): 2401–31. https://doi.org/10.1287/mnsc.2021.3984.Search in Google Scholar

Anscombe, F. J., and W. J. Glynn. 1983. “Distribution of the Kurtosis Statistic B 2 for Normal Samples.” Biometrika 70 (1): 227–34. https://doi.org/10.2307/2335960.Search in Google Scholar

Baruník, J., and T. Kley. 2019. “Quantile Coherency: A General Measure for Dependence between Cyclical Economic Variables.” The Econometrics Journal 22 (2): 131–52. https://doi.org/10.1093/ectj/utz002.Search in Google Scholar

Bohl, M. T., A. Pütz, and C. Sulewski. 2021. “Speculation and the Informational Efficiency of Commodity Futures Markets.” Journal of Commodity Markets 23: 100159. https://doi.org/10.1016/j.jcomm.2020.100159.Search in Google Scholar

Bosch, D., and K. Smimou. 2022. “Traders’ Motivation and Hedging Pressure in Commodity Futures Markets.” Research in International Business and Finance 59: 101529. https://doi.org/10.1016/j.ribaf.2021.101529.Search in Google Scholar

Brittain, L., P. Garcia, and S. H. Irwin. 2011. “Live and Feeder Cattle Options Markets: Returns, Risk, and Volatility Forecasting.” Journal of Agricultural and Resource Economics 36 (1): 28–47.Search in Google Scholar

Chavleishvili, S. and Manganelli, S. 2019. “Forecasting and Stress Testing with Quantile Vector Autoregression.” Available at SSRN 3489065.10.2139/ssrn.3489065Search in Google Scholar

D’Agostino, R. B. 1970. “Transformation to Normality of the Null Distribution of G1.” Biometrika: 679–81. https://doi.org/10.2307/2334794.Search in Google Scholar

Dimpfl, T., M. Flad, and R. C. Jung. 2017. “Price Discovery in Agricultural Commodity Markets in the Presence of Futures Speculation.” Journal of Commodity Markets 5: 50–62. https://doi.org/10.1016/j.jcomm.2017.01.002.Search in Google Scholar

ERS–USDA. 2021. Documentation for the Farm Sector Financial Ratios. https://www.ers.usda.gov/data-products/farm-income-and-wealth-statistics/documentation-for-the-farm-sector-financial-ratios/ (accessed December, 2023).Search in Google Scholar

Fei, C., D. Vedenov, R. B. Stevens, and D. Anderson. 2021. “Single-Commodity vs. Joint Hedging in Cattle Feeding Cycle: Is Joint Hedging Always Essential?” Journal of Agricultural and Resource Economics 46 (3): 464–78.Search in Google Scholar

Fousekis, P. 2023. “Futures Prices Linkages in the Us Soybean Complex.” Applied Finance Letters 12 (1): 119–30. https://doi.org/10.24135/afl.v12i2.697.Search in Google Scholar

Fousekis, P., and V. Grigoriadis. 2022. “Conditional Tail Price Risk Spillovers in Coffee Markets Across Quality, Physical Space, and Time: Empirical Analysis with Penalized Quantile Regressions.” Economic Modelling 106: 105691. https://doi.org/10.1016/j.econmod.2021.105691.Search in Google Scholar

Good, D. L., S. H. Irwin, and O. Isengildina. 2006. “The Value of Usda Situation and Outlook Information in Hog and Cattle Markets.” Journal of Agricultural and Resource Economics 31 (2): 262–82.Search in Google Scholar

Haley, M., and K. Jones. 2017. Livestock, Dairy, and Poultry Outlook. Washington, DC: Economic Research Service: United States Department of Agriculture.Search in Google Scholar

Karmakar, M., and U. Sharma. 2020. “Measuring Quantile Risk Hedging Effectiveness: A Go-Garch-Evt-Copula Approach.” Applied Economics 52 (48): 5244–62. https://doi.org/10.1080/00036846.2020.1761535.Search in Google Scholar

Koenker, R., and G. Bassett Jr. 1978. “Regression Quantiles.” Econometrica: Journal of the Econometric Society: 33–50. https://doi.org/10.2307/1913643.Search in Google Scholar

Koontz, S. R., M. A. Hudson, and M. W. Hughes. 1992. “Livestock Futures Markets and Rational Price Formation: Evidence for Live Cattle and Live Hogs.” Journal of Agricultural and Applied Economics 24 (1): 233–49. https://doi.org/10.1017/s0081305200026157.Search in Google Scholar

Lien, D., K. Shrestha, and J. Wu. 2016. “Quantile Estimation of Optimal Hedge Ratio.” Journal of Futures Markets 36 (2): 194–214. https://doi.org/10.1002/fut.21712.Search in Google Scholar

Mensi, W., X. V. Vo, and S. H. Kang. 2021. “Multiscale Spillovers, Connectedness, and Portfolio Management Among Precious and Industrial Metals, Energy, Agriculture, and Livestock Futures.” Resources Policy 74: 102375. https://doi.org/10.1016/j.resourpol.2021.102375.Search in Google Scholar

Mujtaba, G., A. Siddique, N. Naifar, and S. J. H. Shahzad. 2024. “Hedge and Safe Haven Role of Commodities for the US and Chinese Equity Markets.” International Journal of Finance & Economics 29 (2): 2381–414, https://doi.org/10.1002/ijfe.2788.Search in Google Scholar

Panagiotou, D., and A. Tseriki. 2020. “Assessing the Relationship Between Closing Prices and Trading Volume in the Us Livestock Futures Markets: A Quantile Regressions Methodology.” Studies in Economics and Finance 37 (3): 413–28. https://doi.org/10.1108/sef-09-2019-0352.Search in Google Scholar

Politis, D. N., and J. P. Romano. 1994. “The Stationary Bootstrap.” Journal of the American Statistical Association 89 (428): 1303–13. https://doi.org/10.2307/2290993.Search in Google Scholar

Schroeder, T. C., and B. K. Goodwin. 1991. “Price Discovery and Cointegration for Live Hogs.” The Journal of Futures Markets (1986–1998) 11 (6): 685. https://doi.org/10.1002/fut.3990110604.Search in Google Scholar

Schroeder, T. C., G. T. Tonsor, and B. K. Coffey. 2019. “Commodity Futures with Thinly Traded Cash Markets: The Case of Live Cattle.” Journal of Commodity Markets 15: 100077, https://doi.org/10.1016/j.jcomm.2018.09.005.Search in Google Scholar

Umar, Z., F. Jareño, and A. Escribano. 2022. “Dynamic Return and Volatility Connectedness for Dominant Agricultural Commodity Markets During the Covid-19 Pandemic Era.” Applied Economics 54 (9): 1030–54. https://doi.org/10.1080/00036846.2021.1973949.Search in Google Scholar

Yang, J., Z. Li, and H. Miao. 2021. “Volatility Spillovers in Commodity Futures Markets: A Network Approach.” Journal of Futures Markets 41 (12): 1959–87. https://doi.org/10.1002/fut.22270.Search in Google Scholar

Received: 2024-03-10
Accepted: 2024-12-21
Published Online: 2025-02-07

© 2025 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 17.9.2025 from https://www.degruyterbrill.com/document/doi/10.1515/jafio-2024-0059/html
Scroll to top button