Work

A spatially-continuous neural network temperature model for weather derivatives evaluation

Year: 2025

Type: article

Abstract: This manuscript develops a spatially continuous temperature model for pricing weather derivatives. We formulate temperature at any specific point as a function of its longitude and latitude coordinates. Using satellite climate data, we learn this function using deep neural networks and use it to model and forecast temperature. More specifically, our model allows for describing temperature dynamics at any point within the zone of interest, even when there is no available temperature data at that specific location. This approach enhances our ability to evaluate climate risk with greater precision across different regions. Furthermore, we design a neural network architecture that maintains model explainability, which is crucial for promoting the use of these methods in real-life applications. Through numerical experiments with NASA-MERRA-2 satellite data, we illustrate the model’s application in pricing Heating Degree Day (HDD) derivatives. Additionally, we explore potential extensions of the model to improve forecasting accuracy further.

Source: Annals of Operations Research

DOI: 10.1007/s10479-025-06624-6

Authors: Maria Carannante, Stefania Corsaro, Vincenzo Di Sauro, Zelda Marino, Salvatore Scognamiglio

Institution: Parthenope University of Naples

Language: English

Topic: Neural Networks and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Climate action

Open Access status: hybrid

Estimated APC paid: $3,090