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Predicting and Mitigating Agricultural Price Volatility Using Climate Scenarios and Risk Models

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  • Sourish Das
  • Sudeep Shukla
  • Abbinav Sankar Kailasam
  • Anish Rai
  • Anirban Chakraborti

Abstract

Agricultural price volatility challenges sustainable finance, planning, and policy, driven by market dynamics and meteorological factors such as temperature and precipitation. In India, the Minimum Support Price (MSP) system acts as implicit crop insurance, shielding farmers from price drops without premium payments. We analyze the impact of climate on price volatility for soybean (Madhya Pradesh), rice (Assam), and cotton (Gujarat). Using ERA5-Land reanalysis data from the Copernicus Climate Change Service, we analyze historical climate patterns and evaluate two scenarios: SSP2.4.5 (moderate case) and SSP5.8.5 (severe case). Our findings show that weather conditions strongly influence price fluctuations and that integrating meteorological data into volatility models enhances risk-hedging. Using the Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model, we estimate conditional price volatility and identify cross-correlations between weather and price volatility movements. Recognizing MSP's equivalence to a European put option, we apply the Black-Scholes model to estimate its implicit premium, quantifying its fiscal cost. We propose this novel market-based risk-hedging mechanism wherein the government purchases insurance equivalent to MSP, leveraging Black-Scholes for accurate premium estimation. Our results underscore the importance of meteorological data in agricultural risk modeling, supporting targeted insurance and strengthening resilience in agricultural finance. This climate-informed financial framework enhances risk-sharing, stabilizes prices, and informs sustainable agricultural policy under growing climate uncertainty.

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  • Sourish Das & Sudeep Shukla & Abbinav Sankar Kailasam & Anish Rai & Anirban Chakraborti, 2025. "Predicting and Mitigating Agricultural Price Volatility Using Climate Scenarios and Risk Models," Papers 2503.24324, arXiv.org.
    • Handle: RePEc:arx:papers:2503.24324
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    1. K.S. Aditya & S.P. Subash & K.V. Praveen & M.L. Nithyashree & N. Bhuvana & Akriti Sharma, 2017. "Awareness about Minimum Support Price and Its Impact on Diversification Decision of Farmers in India," Asia and the Pacific Policy Studies, Wiley Blackwell, vol. 4(3), pages 514-526, September.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. Robert C. Merton, 2005. "Theory of rational option pricing," World Scientific Book Chapters, in: Sudipto Bhattacharya & George M Constantinides (ed.), Theory Of Valuation, chapter 8, pages 229-288, World Scientific Publishing Co. Pte. Ltd..
    4. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    5. Birthal, P.S. & Khan, T.M. & Negi, D.S. & Agarwal, S., 2014. "Impact of Climate Change on Yields of Major Food Crops in India: Implications for Food Security," Agricultural Economics Research Review, Agricultural Economics Research Association (India), vol. 27(2).
    6. Ashok Kumar & Abbinav Sankar Kailasam & Anish Rai & Sudeep Shukla & Sourish Das & Anirban Chakraborti, 2025. "The Impact of Meteorological Factors on Crop Price Volatility in India: Case studies of Soybean and Brinjal," Papers 2503.11690, arXiv.org, revised Mar 2025.
    7. Anirban Chakraborti & Hrishidev & Kiran Sharma & Hirdesh K. Pharasi, 2019. "Phase separation and scaling in correlation structures of financial markets," Papers 1910.06242, arXiv.org, revised Jul 2020.
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