Climate-Finance Integration: Modeling and Pricing CDD Options Using Chicagos Historical Temperature Data and OU-Monte Carlo Hybrid Simulation

Climate change has heightened weather volatility, raising financial risks for temperature-sensitive industries and creating a need for more accurate weather derivative pricing. This paper develops an integrated computational framework for modeling and pricing Cooling Degree Day (CDD) options based on Python, with a focus on improving the accuracy and reliability of weather risk assessment. Based on Chicago's historical temperature data, the paper establishes a robust two-stage modeling approach that combines statistical methods with financial engineering. The initial stage characterizes mean-reverting behavior in temperature anomalies through comprehensive analysis of daily and monthly temperature averages. A joint seasonal autoregressive integral sliding average model (SARIMA) and Ornstein-Uhlenbeck (OU) process were used to capture dynamic temperature patterns and derive accurate process parameters. Subsequent analyses employ Monte Carlo simulations within Python, generating 10,000 potential temperature trajectories to compute cumulative summer cooling degree day (CDD) values and ascertain option prices. Findings demonstrate the model's effectiveness in identifying and quantifying mean-reversion behavior in temperature time series. The simulated temperature paths realistically incorporate seasonal variations and random fluctuations. The resulting option price estimates reliably reflect underlying temperature risks.