2024/11/18 by Marco Hening-Tallarico, Tallarico, Marco Hening, Pablo Olivares +1
Environmental Science · #27M10 #68T09 #FOS: Computer and information sciences #FOS: Economics and business #G.3 #Hydrology and Drought Analysis #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Mathematical Finance (q-fin.MF) #Statistical Finance (q-fin.ST)
paper · pdf · doi:10.48550/arxiv.2411.12013
openalex publication_date 2024/11/18 · openalex created_date 2024/11/21 · openalex updated_date 2026/07/28
This paper studies pricing of weather-derivative (WD) contracts on temperature and precipitation. For temperature-linked strangles in Toronto and Chicago, we benchmark a harmonic-regression/ARMA model against a feed-forward neural network (NN), finding that the NN reduces out-of-sample mean-squared error (MSE) and materially shifts December fair values relative to both the time-series model and the industry-standard Historic Burn Approach (HBA). For precipitation, we employ a compound Poisson--Gamma framework: shape and scale parameters are estimated via maximum likelihood estimation (MLE) and via a convolutional neural network (CNN) trained on 30-day rainfall sequences spanning multiple seasons. The CNN adaptively learns season-specific (α,β) mappings, thereby capturing heterogeneity across regimes that static i.i.d. fits miss. At valuation, we assume days are i.i.d. Γ(α,β) within each regime and apply a mean-count approximation (replacing the Poisson count by its mean (nλ) to derive closed-form strangle prices. Exploratory analysis of 1981--2023 NASA POWER data confirms pronounced seasonal heterogeneity in (α,β) between summer and winter, demonstrating that static global fits are inadequate. Back-testing on Toronto and Chicago grids shows that our regime-adaptive CNN yields competitive valuations and underscores how model choice can shift strangle prices. Payoffs are evaluated analytically when possible and by simulation elsewhere, enabling a like-for-like comparison of forecasting and valuation methods.