• Dejan Živkov Novi Sad school of business, University of Novi Sad, Vladimira Perića Valtera 4, 21000 Novi Sad, Serbia https://orcid.org/0000-0003-2357-3250
  • Nikola Jančev Novi Sad school of business, University of Novi Sad, Vladimira Perića Valtera 4, 21000 Novi Sad, Serbia https://orcid.org/0000-0003-1280-9184
  • Đorđe Alavuk Novi Sad school of business, University of Novi Sad, Vladimira Perića Valtera 4, 21000 Novi Sad, Serbia https://orcid.org/0000-0003-4869-0330
  • Dragana Bolesnikov Novi Sad school of business, University of Novi Sad, Vladimira Perića Valtera 4, 21000 Novi Sad, Serbia https://orcid.org/0000-0001-9495-9439




livestock commodities, parametric and historical Valueat-Risk


This paper tries to assess the level of losses that investors in four livestock commodities might have. The analysis comprises live cattle, feeder cattle, lean hogs and milk class III, and for the risk calculation, we use parametric and historical VaR measures. Full sample is divided into pre-crisis and crisis subsamples. According to the results, lean hogs are the riskiest asset in the pre-crisis period, regarding both parametric and historical VaR. In the crisis period, milk is the riskiest asset in terms of parametric VaR in all probability levels. However, in terms of historical VaR, lean hogs have the highest potential of loses between 90-97% VaR, but at 99% VaR, milk takes upper hand. In the crisis period, the level of losses for lean hogs and milk exceeds 4% in one day at 99% probability, which means that these commodities should be hedged if investors want to avoid great losses. The results indicate that parametric VaR significantly deviates from historical VaR in both subsamples, which means that investors in livestock commodities should use historical VaR for downside risk measurement.


Download data is not yet available.


Aloui, C., Hamida, H.B. (2015). Estimation and performance assessment of Valueat-Risk and expected shortfall based on long-memory GARCH-class models. Finance a uvěr-Czech Journal of Economics and Finance, 65(1), 30-54.

Barndorff-Nielsen, O. E. (1997). Normal inverse Gaussian distributions and stochastic volatility modelling. Scandinavian Journal of Statistics, 24(1), 1–13. DOI: 10.1111/1467-9469.00045

Bina, J.D., Schroeder, T.C., Tonsor, G.T. (2022). Conditional feeder cattle hedge ratios: Cross hedging with fluctuating corn prices. Journal of Commodity Markets, 26, 100193. DOI: 10.1016/j.jcomm.2021.100193

Chai, S., Zhou, P. (2018). The Minimum-CVaR strategy with semi-parametric estimation in carbon market hedging problems. Energy Economics, 76, 64–75. DOI: 10.1016/j.eneco.2018.09.024

Chen, J., Xu, L., Xu, H. (2022). The impact of COVID-19 on commodity options market: Evidence from China. Economic Modelling, 116, 105998. DOI: 10.1016/j.econmod.2022.105998

Dogan, E., Majeed, M.T., Luni, T. (2022). Analyzing the nexus of COVID-19 and natural resources and commodities: Evidence from time-varying causality. Resources Policy, 77, 102694. DOI: 10.1016/j.resourpol.2022.102694

Gong, X., Xu, J. (2022). Geopolitical risk and dynamic connectedness between commodity markets. Energy Economics, 110, 106028. DOI: 10.1016/j.eneco.2022.106028

Kuzman, B., Petković, B., Petković, D. (2021). Evaluation of optimal economic and technical indicators for agriculture stock trading decision. International Journal of Economic Practice and Policy, 18(2), 124-140. DOI: 10.5937/skolbiz2-34986

Morgan, W., Cotter, J., Dowd, K. (2012). Extreme Measures of Agricultural Financial Risk.Journal of Agricultural Economics, 63, 65–82. DOI:10.1111/j.1477-9552.2011.00322.x

Rawtani, D., Gupta, G., Khatri, N., Rao, P.K., Hussain, C.M. (2022). Environmental damages due to war in Ukraine: A perspective. Science of the Total Environment, 850, 157932. DOI: 10.1016/j.scitotenv.2022.157932

Rehman, A., Jian, W., Khan, N., Saqib, R. (2018). Major crops market risk based on Value at Risk model in P.R. China. Sarhad Journal of Agriculture, 34, 435-442. DOI: 10.17582/journal.sja/2018/34.2.435.442

Saâdaoui, F., Jabeur, S.B., Goodell, J.W. (2022): Causality of geopolitical risk on food prices: Considering the Russo–Ukrainian conflict. Finance Research Letters, 49, 103103. DOI: 10.1016/j.frl.2022.103103

So, M.K.P., Yu, P.L.H. (2006). Empirical analysis of GARCH models in value at risk estimation. International Financial Markets, Institution and Money, 16, 180–197. DOI: 10.1016/j.intfin.2005.02.001

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. Energy Economics, 113, 106235. DOI: 10.1016/j.eneco.2022.106235

Tuncer, G. (2022). The relationship between agricultural raw materials and oil price: An empirical analysis. Ekonomika poljoprivrede, 69(4), 975-989. DOI: 10.5937/ekoPolj2204975G

Xouridas, S. (2015). Agricultural Financial Risks Resulting from Extreme Events. Journal of Agricultural Economics, 66, 192–220. DOI: 10.1111/1477-9552.12083

Xu, Q., Jin, B., Cuixia Jiang, C. (2021). Measuring systemic risk of the Chinese banking industry: A wavelet-based quantile regression approach. North American Journal of Economics and Finance, 55, 101354. DOI: 10.1016/j.najef.2020.101354

Živkov, D. Joksimović, M., Balaban, S. (2021). Measuring parametric and semiparametric downside risk of selected agricultural commodities. Agricultural Economics – Zemedelska Ekonomika, 67(8), 305-315. DOI: 10.17221/148/2021-AGRICECON




How to Cite

Živkov, D., Jančev, N., Alavuk, Đorđe, & Bolesnikov, D. (2023). RISK EVALUATION OF LIVESTOCK COMMODITIES – VALUE-AT-RISK APPROACH . Economics of Agriculture, 70(4), 967–980. https://doi.org/10.59267/ekoPolj2304967Z



Original scientific papers