Modeling the Daily Average Temperature Data Using Stochastic Process and Neural Networks for Weather Derivatives

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Weather derivatives are financial instruments influenced by temperature fluctuations, impacting industries such as agriculture, tourism, and energy. Accurate temperature modeling is essential for improving risk assessment and hedging strategies. This study evaluates the effectiveness of two forecasting hybrid approaches: the Fourier Ornstein-Uhlenbeck (OU) process, a widely used stochastic model, and the Fourier-Elman Recurrent Neural Network (ERNN), a hybrid neural network-based model. Daily temperature data from Chiang Mai, Thailand, spanning January 2005 to December 2021, were analyzed. The predictive performance of each model was assessed using root mean square error (RMSE). The results indicate the Fourier ERNN model (RMSE = 0.106) significantly outperforms the Fourier OU process (RMSE = 2.299), demonstrating superior accuracy in capturing both seasonal and stochastic variations in temperature dynamics. Thus, deep learning-based hybrid models provide a more effective framework for temperature forecasting. The proposed approach has potential applications in climate risk management, weather derivative pricing, and decision-making in climate-sensitive sectors.