Quantifying Seasonal Weather Risk in Indian Markets: Stochastic Model for Risk-Averse State-Specific Temperature Derivative Pricing

Abstract

This technical report presents a stochastic framework for pricing temperature derivatives in Indian markets accounting for both monsoon and winter seasons. Utilising historical temperature and electricity consumption data from 12 Indian states we develop a model based on a modified mean-reverting Ornstein-Uhlenbeck process and employ Monte Carlo simulations for pricing. Our analysis reveals significant variations in option pricing across states with monsoon call options ranging from 10.78 USD to 182.82 USD and winter put options from 48.65 USD to 194.99 USD. The introduction of a risk aversion parameter shows substantial impacts on pricing leading to an increase of up to 416 percentage in option prices for certain states. Sensitivity analyses indicate that option prices are more responsive to changes in volatility than to mean reversion rates. Additionally extreme weather scenarios can shift option prices by up to 409 percentage during heatwaves and decrease by 60 percentage during cold waves. These findings emphasise the importance of state-specific and season-specific approaches in temperature derivative pricing highlighting the need for tailored risk management strategies in India's diverse climate.