The Impact of Meteorological Factors on Crop Price Volatility in India: Case studies of Soybean and Brinjal
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ID: 282051
2025
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Abstract
Climate is an evolving complex system with dynamic interactions and
non-linear feedback mechanisms, shaping environmental and socio-economic
outcomes. Crop production is highly sensitive to such climatic fluctuations.
This paper studies the price volatility of agricultural crops as influenced by
meteorological variables (and many other environmental, social and governance
factors), which is a critical challenge in sustainable finance, agricultural
planning, and policy-making. As case studies, we choose the two Indian states
of Madhya Pradesh (for Soybean) and Odisha (for Brinjal). We employ an
Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH)
model to estimate the conditional volatility of the log returns of crop prices
from 2012 to 2024. This study further explores the cross-correlations between
volatility and the meteorological variables. Further, a Granger-causality test
is carried out to analyze the causal effect of meteorological variables on the
price volatility. Finally, the Seasonal Auto-Regressive Integrated Moving
Average with Exogenous Regressors (SARIMAX) and Long Short-Term Memory (LSTM)
models are implemented as simple machine learning models of price volatility
with meteorological factors as exogenous variables. We believe that this will
illustrate the usefulness of simple machine learning models in agricultural
finance, and help the farmers to make informed decisions by considering climate
patterns and making beneficial decisions with regard to crop rotation or
allocations. In general, incorporating meteorological factors to assess
agricultural performance could help to understand and reduce price volatility
and possibly lead to economic stability.
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| Authors | Ashok Kumar; Abbinav Sankar Kailasam; Anish Rai; Sudeep Shukla; Sourish Das; Anirban Chakraborti |
| Journal | arXiv |
| Year | 2025 |
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