Scholarlink

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.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Calibrating Temperature Models with Neural Networks for Weather Derivatives

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Stefania Corsaro,
  • Vincenzo Di Sauro,
  • Zelda Marino,
  • Salvatore Scognamiglio

摘要

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.