Use of maximum entropy in estimating production risks in crop farms

Kevorchian, Cristian and Gavrilescu, Camelia (2015): Use of maximum entropy in estimating production risks in crop farms. Published in: Agricultural Economics and Rural Development - Realities and Perspectives for Romania , Vol. 6, No. ISSN 2285–6803 ISSN-L 2285–6803 (20 November 2015): pp. 142-147.

Abstract

The entropic value of the production risk is closely linked to the farmer’s aversion to this type of risk. Since risk aversion is difficult to quantify, it is preferable to use the MaxEnt model as a quantitative benchmark in assessing and covering the production risk through adequate financial resources. The classification of the Selyaninov index value as measure of the production risk based on the MaxEnt model utilization makes it possible to evaluate the production risk and the transfer decision to an adequate market implicitly. The authors’ previous research investigated the risk coverage through derivative financial instruments that diminish the farmer’s exposure to the production risk; the present paper adds to previous research by investigating an equally important issue: sizing the risk that is the object of coverage. Through the utilization of the stochastic methods in estimating the risk measure, a less rigid method is obtained that can be adapted and applied to the risk management processes in agriculture.

• Item Type: MPRA Paper
• Language: English
• Keywords: Production risk, crop farms, Markov models, MaxEnt
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C12 - Hypothesis Testing: General; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63 - Computational Techniques ; Simulation Modeling; D - Microeconomics > D8 - Information, Knowledge, and Uncertainty > D81 - Criteria for Decision-Making under Risk and Uncertainty; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q1 - Agriculture > Q12 - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets

References

Alam, J.A.T.M, Rahman, S.M., Saadat, A.H.M. (2013). Monitoring meteorological and agricultural drought dynamics in Barind region Bangladesh using standard precipitation index and Markov chain model. International Journal of Geomatics and Geosciences, 3(3), 512-524.

Hurduzeu, G., Kevorchian, C., Gavrilescu, C., Hurduzeu, R. (2014). Hazards and Risks in the Romanian Agriculture Due to Climate Changes. Procedia Economics and Finance, Elsevier, vol.8, 346-352.

Kannan, Senthamarai K. & Farook, Jawahar A. (2015). Stochastic Simulation of Precipitation Using Markov Chain - Mixed Exponential Model. Applied Mathematical Sciences, 65(9), 3205 - 3212.

Kevorchian, C., Gavrilescu, C., & Hurduzeu, G. (2013). Qualitative Risk Coverage in Agriculture through Derivative Financial Instruments Based on Selyaninov Indices. Financial Studies, 3 (17), 19-32.

Rutten, L. (2012). Innovative agricultural finance and risk management. Strengthening food production and trade in the transition region. FAO Investment Center, Rome, working paper prepared under the FAO/EBRD Cooperation, Retrieved September 10, 2015, from http://www.fao.org/3/a-at658e.pdf.

World Bank (2011). Weather Index Insurance for Agriculture: Guidance for Development Practitioners. Agriculture and rural development discussion paper 50 (66274). Washington, DC: The International Bank for Reconstruction and Development & The World Bank, Retrieved September 10, 2015, from http://documents.worldbank.org/curated/en/2011/11/15643352/weather-index-insurance-agriculture-guidance-development-practitioners.