QUANTIFYING FINANCIAL EXPOSURE WITHIN AGRICULTURAL ENTERPRISES: METHODOLOGICAL PARADIGMS AND RISK MANAGEMENT STRATEGIES

Abstract

The article is devoted to the critical problem of financial exposure quantification in modern capital-intensive agribusiness. The author presents the “Kavun Strategy” – a hybrid methodological paradigm that transcends traditional linear models, synthesizing Extreme Value Theory (EVT), copula-based dependency modeling and stochastic Monte Carlo simulation (10 000 iterations). Through the case study of an integrated U.S. holding (Iowa), it is proven that the standard Value-at-Risk (VaR) metric substantially underestimates “black swan” risks due to the leptokurtic nature of agricultural markets. The results demonstrate that structural hedging – the integration of federal revenue protection insurance with exchange-traded derivatives, reduces tail risk (Expected Shortfall) by 68.2%. The study also reveals the role of “Agriculture 4.0” technologies, including digital twins and Explainable AI (XAI), in forming the systemic financial immunity of the enterprise.

References

1. V C, Sanjay & Uppar, Jyoti & H R, Ragini. (2024). Risk Management in Agriculture.  2. Maureen, Okeke & Adegbite, Yetunde. (2025). Between Capital Intensity and Financial Performance: Insights from Quoted Agricultural Firms in Nigeria. 10.56201/ijefm.v10.no10.2025.pg326.341.

3. Cédric Crofilsa, Ewen Gallic and Gauthier Vermandel. (2024). The Dynamic Effects of Weather Shocks on Agricultural Production. Journal of Environmental Economics and Management, 130, 103078. doi: 10.1016/j.jeem.2024.103078

4. Azzam, A. M., Ceylan, R. F., & Goodwin, B. K. (2011). Market integration and volatility spillover in U.S. agriculture: Implications for supply chain risk. Agricultural Economics, 42(1), 1–15. https://doi.org/10.1111/j.1574-0862.2010.00501.x

5. Rezitis, Anthony and Kousoulis, Kyriakos N. and Moffatt, Peter G., Tail Risk Spillovers in Commodity Markets: A Copula-Based Value-at-Risk and Expected Shortfall Approach. Available at SSRN: https://ssrn.com/abstract=5609399  or http://dx.doi.org/10.2139/ssrn.5609399

6. Goodwin, B. K., & Kazukauskas, A. (2014). Hedging and the design of crop insurance contracts in the presence of basis risk. American Journal of Agricultural Economics, 96(3), 735–750. https://doi.org/10.1093/ajae/aau021

7. Mendelsohn, R., & Dinar, A. (2009). Climate change and agriculture: An economic analysis of global impacts, adaptation, and distributional effects. Agricultural Economics, 41(S1), 375–391. https://doi.org/10.1111/j.1574-0862.2009.00482.x

8. Clement, Mateo. (2025). Predictive Risk Assessment Models for Crop Insurance in Climate-Sensitive U.S. Regions.

9. Ivanov, D. (2020). Digital twins and financial resilience in integrated supply chains. International Journal of Production Research, 58(17), 5366–5383.

10. Bosupeng, Mpho & Naranpanawa, Athula & Su, Jen-Je. (2024). "Does exchange rate volatility affect the impact of appreciation and depreciation on the trade balance? A nonlinear bivariate approach," Economic Modelling, Elsevier, vol. 130(C).

11. Meagan Martin. (2020). ECONOMICS WORKING PAPER SERIES Working Paper 2020–05–E U.S. INTERNATIONAL TRADE COMMISSION 500 E Street SW Washington, DC 20436

12. Liang, Weifang & Bryant, Henry & Liu, Yong. (2025). "Modeling Agricultural Risk Dependencies: A Comparison of Copula and Kernel Density Methods for Farm-Level Risk Management," 2025 AAEA & WAEA Joint Annual Meeting, July 27-29, Denver, CO 361101, Agricultural and Applied Economics Association.

13. Bellemare, M. F. (2012). Asymmetric information and price volatility in commodity markets. Journal of Agricultural & Food Industrial Organization, 10(1), 1–23.

14. Phillips, M. (2007). The urban–rural divide. Journal of Rural Studies, 23(1), 111–123. https://doi.org/10.1016/j.jrurstud.2006.10.011

15. DeLay, Nathan. (2019). The Impact of Federal Crop Insurance on the Conservation Reserve Program. Agricultural and Resource Economics Review. 48. 297-327. 10.1017/age.2019.9.

16. Hull, J. C. (2018). Options, Futures, and Other Derivatives (10th ed.). Pearson.

17. Jorion, P. (2007). Value at Risk: The New Benchmark for Managing Financial Risk. McGraw-Hill Education.

18. Just, R.E. and Pope, R.D. (1979) Production Function Estimation and Related Risk Considerations. American Journal of Agricultural Economics, 61, 276-284. https://doi.org/10.2307/1239732

19. Rockafellar, R. T., & Uryasev, S. (2000). Optimization of conditional value-at-risk. Journal of Risk, 2(3), 21–41.

20. Turvey, C. G. (2001). Weather derivatives for specific event risks in agriculture. Review of Agricultural Economics, 23(2), 333–351.

21. Myers, R. J. (1991). Estimating time-varying optimal hedge ratios on futures markets. Journal of Futures Markets, 11(1), 39–53.

22. Artzner, P., Delbaen, F., Eber, J. M., & Heath, D. (1999). Coherent measures of risk. Mathematical Finance, 9(3), 203–228.

23. Sklar, M. (1959). Fonctions de répartition à n dimensions et leurs marges. Publ. Inst. Statis. Univ. Paris, 8, 229–231.

24. Goodwin, B. K., & Ker, A. P. (1998). Nonparametric estimation of crop yield distributions. American Journal of Agricultural Economics, 80(1), 139–153.

25. Goodwin, B. K., & Smith, V. H. (2016). What harm is done by speculation? Journal of Agricultural and Resource Economics, 41(1), 1–27.

26. Glauber, J. W. (2020). Crop insurance and the 2018 Farm Bill. Applied Economic Perspectives and Policy, 42(1), 5–22.

27. Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77–91.

28. Aboud Neta, Sumaia & Bias, Edilson. (2021). Remote Sensing for Risk Mitigation in Agricultural Financings: Multitemporal Change Detections in Agricultural Areas using the Delta NIR and Delta NDVI Models. International Journal of Advanced Engineering Research and Science. 8. 276-297. 10.22161/ijaers.88.32.

29. Adenekan, Tobiloba. (2025). Explainable AI (XAI) in Financial Decision-Making Systems.

30. Kovalchuk, A. (2026). Complex model of business consulting for small and medium-sized enterprises. Internauka Publishing House. 98 р. URL: https://www.inter-nauka.com/uploads/public/17690670557224.pdf