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Pricing Weather Derivatives: A Time Series Neural Network Approach

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  • Marco Hening-Tallarico
  • Pablo Olivares

Abstract

The objective of the paper is to price weather derivative contracts based on temperature and precipitation as underlying climate variables. We use a neural network approach combined with time series forecast to value Pacific Rim index in Toronto and Chicago

Suggested Citation

  • Marco Hening-Tallarico & Pablo Olivares, 2024. "Pricing Weather Derivatives: A Time Series Neural Network Approach," Papers 2411.12013, arXiv.org.
    • Handle: RePEc:arx:papers:2411.12013
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    References listed on IDEAS

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    1. Roberto Buizza & James W. Taylor, 2004. "A comparison of temperature density forecasts from GARCH and atmospheric models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(5), pages 337-355.
    2. Sean D. Campbell & Francis X. Diebold, 2005. "Weather Forecasting for Weather Derivatives," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 6-16, March.
    3. Pablo Olivares, 2020. "Pricing Temperature Derivatives under a Time-Changed Levy Model," Papers 2005.14350, arXiv.org.
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