Quantifying Seasonal Weather Risk in Indian Markets: Stochastic Model for Risk-Averse State-Specific Temperature Derivative Pricing
Soumil Hooda, Shubham Sharma, Kunal Bansal

TL;DR
This paper develops a stochastic model using a modified Ornstein-Uhlenbeck process with jumps to price weather derivatives in Indian markets, enabling state-specific risk management strategies for temperature fluctuations.
Contribution
It introduces a novel jump-diffusion model calibrated on Indian temperature data for accurate derivative pricing and tailored hedging strategies.
Findings
HDD options in colder states are significantly more expensive.
Volatility increases lead to higher option prices, with a 20% increase raising prices by 4.2%.
State-specific hedging strategies effectively manage temperature risks.
Abstract
This technical report presents a stochastic model for pricing weather derivatives and devising hedging strategies tailored to Indian markets. We model temperature dynamics using a modified Ornstein-Uhlenbeck process with jumps to account for sudden shocks, such as heatwaves and coldwaves. Historical data from 12 Indian states (1951-2023) is used for calibration, and Monte Carlo simulations are employed under the risk-neutral measure to price Heating Degree Days (HDD), Cooling Degree Days (CDD), and extreme event options. Sensitivity analysis reveals that a 20% increase in volatility leads to an approximate 4.2% increase in option prices, highlighting the critical impact of volatility on derivative pricing. Results show that HDD options in colder states like Himachal Pradesh are significantly more expensive, with prices reaching up to INR 684,693, while CDD options in hotter states like…
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Taxonomy
TopicsAgricultural risk and resilience · Market Dynamics and Volatility · Insurance and Financial Risk Management
