Calibrating Temperature Models with Neural Networks for Weather Derivatives

Stefania Corsaro (), Vincenzo Di Sauro (), Zelda Marino () and Salvatore Scognamiglio ()
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Stefania Corsaro: Parthenope University of Naples, Department of Management and Quantitative Studies
Vincenzo Di Sauro: Parthenope University of Naples, Department of Economics and Legal Studies
Zelda Marino: Parthenope University of Naples, Department of Management and Quantitative Studies
Salvatore Scognamiglio: Parthenope University of Naples, Department of Management and Quantitative Studies

A chapter in New Perspectives in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2025, pp 96-107 from Springer

Abstract: Abstract Weather significantly affects business activities, making hedging against related risks crucial; weather derivatives can help mitigate financial impacts. Most of the derivatives currently traded are linked to a temperature; having a good model for its evolution is the backbone of effective weather derivative pricing. This paper presents a novel neural network approach for jointly calibrating the mean and variance of temperature for weather derivatives pricing. We also address the challenge of explainability in neural networks by designing the architecture to replace the approach proposed in [1]. Additionally, we explore potential extensions of the model to improve forecasting accuracy further. Through numerical experiments with weather station data from Fiumicino Maccarese, we illustrate the model’s application in pricing Heating Degree Day futures.

Keywords: Deep Neural Networks; Explainable Deep Learning; climate risk; weather derivatives (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-032-05551-4_9

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DOI: 10.1007/978-3-032-05551-4_9

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