Paper: arXiv 2503.24324

Authors: Sourish Das, Sudeep Shukla, Abbinav Sankar Kailasam, Anish Rai, Sejal Garg, Anirban Chakraborti

Abstract

Agricultural price volatility, driven by market dynamics and meteorological factors such as temperature and precipitation, poses challenges for sustainable finance, planning, and policy. This study analyzes the impact of climate on crop price volatility for soybean in Madhya Pradesh (India) and Illinois (US), rice in Assam (India), wheat in North Dakota (US), cotton in Gujarat (India), and corn in Iowa (US). Using CMIP6 climate projections from the Copernicus Climate Change Service, we examine historical climate patterns and evaluate two future scenarios: SSP2-4.5 (moderate) and SSP5-8.5 (severe). We estimate conditional price volatility using the Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model, and forecast this volatility with a Seasonal Autoregressive Integrated Moving Average with Exogenous Regressors (SARIMAX) model that incorporates meteorological variables. Finally, we apply the Black-Scholes framework to evaluate the cost of put-option-based insurance, which provides protection to farmers against adverse price drops linked to climate change. Our results highlight the role of meteorological data in improving agricultural risk modelling, enabling better design of insurance mechanisms, price stabilization tools, and sustainable policy interventions under climate uncertainty.

Complexity vs Empirical Score

  • Math Complexity: 5.0/10
  • Empirical Rigor: 7.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper effectively links CMIP6 climate projections with econometric models (EGARCH-SARIMAX) and Black-Scholes option pricing to quantify agricultural price risk under various climate scenarios. It provides robust multi-region empirical testing across India and the US, bridging physical climate science with financial risk management in a novel, practical way.

Research Flowchart

  flowchart TD
    A["Research Goal: Mitigate Financial Risk from Climate-Induced Crop Price Volatility"] --> B["Data Inputs: Historical & Future (CMIP6 SSP2-4.5, SSP5-8.5) Climate Data + Crop Prices"]
    B --> C["Estimate Conditional Volatility (EGARCH Model)"]
    C --> D["Forecast Volatility with Climate Regressors (SARIMAX Model)"]
    D --> E["Price Financial Protection Options (Black-Scholes Framework)"]
    E --> F["Outcomes: Enhanced Risk Modeling, Improved Insurance Design & Climate Policy"]

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0 of 65 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.