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Calibrating Temperature Models with Neural Networks for Weather Derivatives

In: New Perspectives in Mathematical and Statistical Methods for Actuarial Sciences and Finance

Author

Listed:
  • 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)

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.

Suggested Citation

  • Stefania Corsaro & Vincenzo Di Sauro & Zelda Marino & Salvatore Scognamiglio, 2025. "Calibrating Temperature Models with Neural Networks for Weather Derivatives," Springer Books, in: Michele La Rocca & Massimiliano Menzietti & Cira Perna & Marilena Sibillo (ed.), New Perspectives in Mathematical and Statistical Methods for Actuarial Sciences and Finance, pages 96-107, Springer.
    • Handle: RePEc:spr:sprchp:978-3-032-05551-4_9
    DOI: 10.1007/978-3-032-05551-4_9
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